ServalSheets

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Production-grade Google Sheets MCP server with 25 tools, 410 actions, safety rails, and enterprise features for spreadsheet automation and data analysis.

автор: khill1269

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Описание

Production-grade Google Sheets MCP server with 25 tools, 410 actions, safety rails, and enterprise features for spreadsheet automation and data analysis.

README

Production-grade Google Sheets MCP Server with 25 tools, 410 actions, safety rails, and enterprise features.

MCP Protocol npm version License: MIT Tests Coverage

Connect to Claude

Option A — Claude.ai connector (no install)

Add as a remote connector in Claude.ai → Settings → Connectors → Add:

https://servalsheets.dev/mcp

Or add to your claude.json:

{
  "mcpServers": {
    "servalsheets": {
      "url": "https://servalsheets.dev/mcp"
    }
  }
}

Option B — Claude Desktop (local, STDIO)

{
  "mcpServers": {
    "servalsheets": {
      "command": "npx",
      "args": ["-y", "servalsheets@latest"]
    }
  }
}

Add to ~/Library/Application Support/Claude/claude_desktop_config.json and restart Claude Desktop. Google OAuth runs on first launch.

Option C — Self-hosted

git clone https://github.com/khill1269/servalsheets
cp .env.example .env   # fill GOOGLE_CLIENT_ID, GOOGLE_CLIENT_SECRET, JWT_SECRET
npm install && npm run build && npm run start:http
# MCP endpoint: http://localhost:3000/mcp

What's New (Post-v2.0.0, 2026-02-17 → Present)

🔧 2026-04-21 Flat-Tool Wire Hardening

✅ Flat→envelope arg shape fix: flat tools (sheets_data_read, etc.) now correctly route through the dispatcher envelope — closes BUG #1.
✅ sheets_discover dispatch wired — catalog tool is now callable (was advertised-but-404) — closes BUG #2.
✅ Flat inputSchemas carry action-specific required params — spreadsheetId / range / sheetName no longer silently dropped on flat calls — closes BUG #3/#6.
✅ sheets_advanced.list_named_functions output validation — response builder no longer trips Zod output guard — closes BUG #4.
✅ sheets_analyze.analyze_data remote-executor gating — stops falsely rejecting in single-process mode — closes BUG #5.
✅ Regression probes pinned: probe-flat-schemas.mjs (7/7) + probe-bug4-5.mjs (5/5) now run against real dist/.
✅ Startup hardening: src/config/env.ts throws instead of process.exit, preflight async checks parallelized, planner catalog deferred out of module load.
✅ MCP UX: sheet_tab resource, structured error _hints, semanticSearch readiness signals.
✅ CI: test-gates.yml hardened, esbuild optionalDep pin.

🧠 LLM Intelligence Sprint, Advanced Compute & Production Hardening

✅ Chain-of-Thought Hints: _hints layer on every sheets_data.read response — data shape, PK detection, formula opportunities, risk level, next-phase routing
✅ Response Intelligence: Quality scanner, action recommender, batching hints, and _meta.apiCallsMade / _meta.executionTimeMs / _meta.quotaImpact on every response
✅ Advanced Compute: DuckDB SQL engine (sql_query, sql_join), Pyodide Python runtime (python_eval, pandas_profile, sklearn_model), formula evaluator (HyperFormula v3.2.0)
✅ Quick Insights + Auto-Fill: sheets_analyze.quick_insights (fast AI-free structural snapshot), sheets_data.auto_fill (pattern-based fill: linear, date, repeat)
✅ O(1) Cache Size Tracking: CacheManager._totalSizeBytes running counter — getStats() / getTotalSize() no longer O(N)
✅ Per-Spreadsheet Throttle: Token-bucket rate limiter per spreadsheetId (configurable via PER_SPREADSHEET_RPS, default 3 RPS)
✅ Plan Encryption: AES-256-GCM agent plan persistence (opt-in via PLAN_ENCRYPTION_KEY)
✅ Webhook DNS Hardening: DNS fail-closed by default (WEBHOOK_DNS_STRICT=true); opt-out for flaky environments
✅ Workspace Events: Google Workspace event subscriptions with 7-day auto-renewal
✅ Scheduler: schedule_create/list/cancel/run_now with node-cron + JSON persistence
✅ Typed Error Classes: All src/handlers/, src/connectors/, src/services/, src/utils/ use typed error classes (ValidationError, ServiceError, ConfigError, NotFoundError, AuthenticationError)
✅ Progress Notifications: 25+ handler actions emit MCP progress notifications for long-running operations

See CHANGELOG.md for complete details.

v2.0.0 (2026-02-17)

🚀 Modern Formula Intelligence & Marketplace Release

✅ Advanced Compatibility Surface: named-function actions remain exposed for compatibility and now return clear FEATURE_UNAVAILABLE guidance when the live Sheets API cannot support them
✅ Spill Range Detection: Find dynamic array formulas via sheets_data.detect_spill_ranges
✅ Formula Presets: XLOOKUP, XMATCH, FILTER, BYROW/BYCOL via sheets_analyze.generate_formula
✅ Marketplace Ready: privacy_policies array in server.json (MCP registry v0.3+)
✅ Knowledge Base: Modern arrays & spill range patterns (src/knowledge/formulas/modern-arrays.md)

Quick Start

# Install globally
npm install -g servalsheets

# Or run directly with npx
npx servalsheets

# Claude Desktop config (~/.claude/claude_desktop_config.json)
{
  "mcpServers": {
    "servalsheets": {
      "command": "npx",
      "args": ["-y", "servalsheets"]
    }
  }
}

On first run, ServalSheets will guide you through Google OAuth authentication.

Claude Desktop connects to the local STDIO process. Hosted HTTP is a separate transport surface for remote deployments and hybrid failover.

Previous Releases

Historical release snapshots are kept here for upgrade context.

v2.0.0 - Enterprise Deployment & Infrastructure (2026-01-26)

Helm Charts, Terraform Modules (AWS/GCP), Health Monitoring
W3C Trace Context, Schema Caching, Webhook Support
Per-User Rate Limiting, VitePress Docs (115+ pages)

v2.0.0 - Zod v4 & Performance (2026-01-10)

Upgraded Zod 3.25 → 4.3.5 with native JSON Schema
14x faster string parsing, 7x faster arrays
57% smaller bundle size

v2.0.0 - MCP Protocol Native (2026-01-06)

MCP Logging, Tasks, Elicitation, Sampling support
6 URI resource templates, Full AbortController cancellation

v2.0.0 - Advanced Analytics (2026-01-05)

Pattern Detection, Column Analysis
AI-Powered formula generation & chart recommendations

Features

Core Capabilities

25 Tools, 409 Actions: Comprehensive Google Sheets API v4 coverage
MCP 2025-11-25 Support: Structured outputs, tasks, prompts, resources, logging, elicitation, and sampling
Multiple Transports: STDIO, Streamable HTTP, and legacy SSE compatibility surface
Safety Rails: Dry-run, effect scope limits, expected state validation, user confirmations
OAuth 2.1 Support: For hosted remote connector deployments

MCP Protocol Support

MCP 2025-11-25 server support includes:

✅ JSON-RPC 2.0: Full compliance via @modelcontextprotocol/sdk v1.29.0
✅ Tools: 25 tools with 410 actions using discriminated unions
✅ Resources: 56 MCP resources + 12 resource templates
- sheets:///{spreadsheetId} - Spreadsheet metadata
- sheets:///{spreadsheetId}/{range} - Range values
- sheets:///{spreadsheetId}/charts - Chart specifications
- sheets:///{spreadsheetId}/charts/{chartId} - Individual chart details
- sheets:///{spreadsheetId}/pivots - Pivot table configurations
- sheets:///{spreadsheetId}/quality - Data quality analysis
- Additional schema, guide, decision, pattern, monitor, and knowledge resources via resources/list
✅ Prompts: 40 guided workflows for common operations
✅ Completions: Argument autocompletion for prompts/resources
✅ Tasks: Background execution with full cancellation support (SEP-1686)
✅ Elicitation: Plan confirmation via sheets_confirm (SEP-1036)
✅ Sampling: AI-powered analysis via sheets_analyze (SEP-1577)
✅ Logging: Dynamic log level control via logging/setLevel handler

Transport Support

✅ STDIO - For Claude Desktop and local CLI usage
✅ Streamable HTTP - For hosted deployments, resumability, and remote access
✅ Legacy SSE Compatibility - Optional compatibility surface for older clients
✅ OAuth 2.1 - Authentication for hosted deployments

Advanced Analytics 🔬

Pattern Detection: Trend analysis, correlations, anomalies, seasonality
Column Profiling: Data type detection, distributions, quality metrics
Statistical Analysis: Comprehensive data quality and structure insights

AI-Powered Features 🤖

Template Suggestions: AI generates contextual spreadsheet templates
Formula Generation: Natural language → Google Sheets formulas
Chart Recommendations: AI suggests optimal visualizations for your data
Requires: Client with MCP sampling capability (SEP-1577)

Performance & Optimization ⚡

Request Deduplication: Prevents duplicate API calls, reduces quota usage
Tiered Diff Engine: Automatic tier selection (METADATA/SAMPLE/FULL)
Rate Limiting: Built-in token bucket with dynamic throttling on 429 errors
Payload Monitoring: Automatic size tracking with 2MB warnings, 10MB hard limits
Batch Efficiency: Real-time optimization analysis and suggestions
HTTP Compression: gzip middleware for bandwidth reduction

Developer Experience 🛠️

Semantic Range Resolution: Query by header name, named ranges, or A1 notation
Intent-Based Architecture: Single BatchCompiler for all mutations
User Confirmations: Elicitation dialogs for destructive operations (SEP-1036)

API Documentation 📚

OpenAPI 3.1 Specification: Auto-generated from Zod schemas
Interactive Swagger UI: Test endpoints at /api-docs when HTTP server is running
Multiple Formats: JSON and YAML exports for SDK generation
25 Tools Documented: Complete API reference with request/response schemas
Authentication Schemes: Bearer token and OAuth 2.0 documented

Access Documentation:

npm run start:http        # Start HTTP server
open http://localhost:3000/api-docs  # View Swagger UI

See OpenAPI Documentation Guide for details.

Getting Started

Installation

npm install servalsheets

Using with Claude Desktop (STDIO)

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "servalsheets": {
      "command": "npx",
      "args": ["servalsheets"],
      "env": {
        "GOOGLE_APPLICATION_CREDENTIALS": "/path/to/service-account.json"
      }
    }
  }
}

Using as Hosted Server (Streamable HTTP)

# Start HTTP server
npm run start:http

# Or with environment variables
PORT=3000 GOOGLE_CLIENT_ID=xxx GOOGLE_CLIENT_SECRET=xxx npm run start:http

Hosted HTTP is for remote deployments and connector flows. Do not point Claude Desktop at a hosted remote server through claude_desktop_config.json. Use Claude's connector UI for the remote connector flow and keep claude_desktop_config.json for local stdio servers only.

Hosted Failover (Optional)

Hosted failover is disabled by default and only turns on when both of these are set:

export MCP_REMOTE_EXECUTOR_URL=https://example.com/mcp
export MCP_REMOTE_EXECUTOR_TOOLS=sheets_compute,sheets_analyze

Only the allowlisted tools in MCP_REMOTE_EXECUTOR_TOOLS are eligible for remote fallback.

OAuth Scope Modes

ServalSheets uses deployment-aware OAuth scopes to balance functionality and Google verification speed:

Mode	Actions Available	Use Case	Google Verification Time
full (default)	410/410	Self-hosted, enterprise	4-6 weeks
standard	Reduced subset	SaaS, marketplace apps	3-5 days
minimal	Basic subset	Basic operations only	3-5 days
readonly	Read-only subset	Analysis/reporting only	3-5 days

Self-Hosted (Default)

All features work out of the box with full scopes:

npm run auth
npm run start:http

SaaS/Marketplace Deployment

For faster Google verification (3-5 days instead of 4-6 weeks):

export DEPLOYMENT_MODE=saas
npm run auth
npm run start:http

Disabled features in standard mode:

Sharing/collaboration (sheets_collaborate)
BigQuery integration (sheets_bigquery)
Apps Script automation (sheets_appsscript)
Webhook notifications (sheets_webhook)

Enable all features: Set OAUTH_SCOPE_MODE=full (accepts longer verification time)

Environment Variables:

DEPLOYMENT_MODE: self-hosted (default, full scopes) or saas (standard scopes)
OAUTH_SCOPE_MODE: Explicit override - full, standard, minimal, readonly

Documentation

ServalSheets has comprehensive documentation organized by use case:

🚀 Getting Started

docs/guides/FIRST_TIME_USER.md - First-time user walkthrough (start here!)
docs/guides/CLAUDE_DESKTOP_SETUP.md - Claude Desktop setup
docs/guides/INSTALLATION_GUIDE.md - Installation instructions
docs/guides/QUICKSTART_CREDENTIALS.md - Quick credentials setup

📖 User Guides

docs/guides/USAGE_GUIDE.md - Complete usage guide
docs/guides/PROMPTS_GUIDE.md - Effective AI prompts
docs/guides/OAUTH_USER_SETUP.md - OAuth authentication
docs/guides/TROUBLESHOOTING.md - Common issues
docs/guides/SKILL.md - Claude skill integration

🏭 Production & Operations

docs/guides/DEPLOYMENT.md - Production deployment
docs/guides/MONITORING.md - Monitoring & observability
docs/guides/PERFORMANCE.md - Performance optimization
SECURITY.md - Security policy

📚 Complete Documentation Index

docs/README.md - Full documentation index with all guides, development docs, and release notes

🔐 Production

SECURITY.md - Security best practices
docs/guides/PERFORMANCE.md - Performance tuning
docs/guides/MONITORING.md - Observability setup
docs/guides/DEPLOYMENT.md - Deployment examples
docs/guides/TROUBLESHOOTING.md - Common issues

Need help? Start with docs/guides/USAGE_GUIDE.md for a complete walkthrough.

Contributing

We welcome contributions! ServalSheets follows strict quality standards to maintain production-grade reliability.

Quick Links

Developer Workflow Guide - Step-by-step guide for contributors
Claude Code Rules - Required rules for all contributions

Quick Start for Contributors

# 1. Clone and install
git clone https://github.com/khill1269/servalsheets.git
cd servalsheets
npm install

# 2. Create feature branch
git checkout -b fix/your-bug-name

# 3. Make changes (≤3 src/ files recommended)
# Edit src/handlers/values.ts

# 4. Verify (must pass before PR)
npm run verify

# 5. Commit and push
git commit -m "fix(values): handle empty arrays gracefully"
git push origin fix/your-bug-name

Core Principles

All contributions must follow these Claude Code Rules:

Verify Before Claiming - Provide file paths + line ranges for all claims
Trace Execution Paths - Document the full call stack
No "Fixes" Without Proof - Write failing test first, then fix
Minimal Change Policy - ≤3 files in src/ per commit
No Silent Fallbacks - Log errors, never return {} silently

Verification Commands

npm run verify              # Full verification pipeline
npm run check:drift         # Metadata synchronization
npm run check:placeholders  # No TODO/FIXME in src/
npm run check:silent-fallbacks  # No silent {} returns
npm run check:debug-prints  # No console.log in src/
npm test                    # Run 8,500+ tests

Before Creating a PR

All tests pass (npm test)
Verification passes (npm run verify)
≤3 src/ files modified (or documented exception)
Evidence provided in commit message
Follows Claude Code Rules

See the Developer Workflow Guide for detailed instructions.

Tools Reference

Tool Summary (25 tools, 410 actions)

Tool	Actions	Description
`sheets_auth`	5	Authentication & OAuth 2.1
`sheets_core`	21	Spreadsheet and sheet metadata/management
`sheets_data`	25	Read/write values, notes, hyperlinks, clipboard, cross-spreadsheet
`sheets_format`	25	Cell formatting, conditional formats, data validation, sparklines
`sheets_dimensions`	30	Rows/columns, filters, sorts, groups, freezes, views, slicers
`sheets_visualize`	18	Charts and pivot tables
`sheets_collaborate`	40	Sharing, comments, versions/snapshots, approvals, labels
`sheets_advanced`	31	Named ranges, protected ranges, metadata, banding, tables, chips
`sheets_transaction`	6	Atomic batch operations (80-95% API savings)
`sheets_quality`	4	Validation, conflicts, impact analysis
`sheets_history`	10	Undo/redo, history, revert, time-travel debugger
`sheets_confirm`	5	Elicitation confirmations & wizards
`sheets_analyze`	22	AI-assisted analysis, suggestions & recommendations
`sheets_fix`	6	Automated fixes & data cleaning pipeline
`sheets_composite`	21	High-level bulk operations, NL sheet generation & ETL pipelines
`sheets_session`	31	Session context, preferences, checkpoints
`sheets_appsscript`	19	Apps Script automation
`sheets_bigquery`	17	BigQuery Connected Sheets
`sheets_templates`	8	Enterprise templates
`sheets_webhook`	10	Webhook registration & delivery
`sheets_federation`	4	Remote MCP server federation & cross-server calls
`sheets_dependencies`	10	Formula dependency analysis & scenario modeling
`sheets_agent`	8	Autonomous multi-step execution with plan/execute/rollback
`sheets_compute`	16	Server-side computation (stats, regression, forecast, matrix ops)
`sheets_connectors`	10	External data connectors (Finnhub, FRED, REST APIs)

Examples

Example 1: Read and Analyze Data

// Read sales data
const result = await sheets_data({
  action: 'read',
  spreadsheetId: '1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgvE2upms',
  range: { a1: 'Sales!A1:D100' },
  valueRenderOption: 'FORMATTED_VALUE',
});

// Analyze data quality
const analysis = await sheets_analyze({
  action: 'analyze_quality',
  spreadsheetId: '1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgvE2upms',
  range: { a1: 'Sales!A1:D100' },
});
// Returns: { completeness: 0.95, duplicates: 3, outliers: [...] }

Example 2: Safe Bulk Update with Dry Run

// Preview changes first (dry run)
const preview = await sheets_data({
  action: 'write',
  spreadsheetId: '1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgvE2upms',
  range: { a1: 'Data!A2:C100' },
  values: newData,
  safety: {
    dryRun: true,
    effectScope: { maxCellsAffected: 500 },
  },
});
// Returns: { dryRun: true, cellsAffected: 297 }

// Execute if safe
if (preview.data.cellsAffected < 500) {
  const result = await sheets_data({
    action: 'write',
    spreadsheetId: '1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgvE2upms',
    range: { a1: 'Data!A2:C100' },
    values: newData,
    safety: {
      expectedState: { rowCount: 100 },
      autoSnapshot: true,
    },
  });
}

Example 3: Semantic Range Queries

// Query by column header instead of A1 notation
const revenue = await sheets_data({
  action: 'read',
  spreadsheetId: '1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgvE2upms',
  range: {
    semantic: {
      sheet: 'Q4 Sales',
      column: 'Total Revenue', // Matches header in row 1
      includeHeader: false,
    },
  },
});

// Returns cell values
// {
//   success: true,
//   action: 'read',
//   values: [[5000], [7500], [3200], ...]
// }

Example 4: Create Charts

// Create a bar chart from data
const chart = await sheets_visualize({
  action: 'create',
  spreadsheetId: '1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgvE2upms',
  sheetId: 0,
  chartType: 'BAR',
  title: 'Monthly Sales',
  data: { sourceRange: { a1: 'Sales!A1:B12' } },
  position: {
    anchorCell: 'Sheet1!F1',
    width: 600,
    height: 400,
  },
});

Example 5: Conditional Formatting

// Add conditional formatting rule
const rule = await sheets_format({
  action: 'add_conditional_format',
  spreadsheetId: '1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgvE2upms',
  sheetId: 0,
  range: { a1: 'Data!B2:B100' },
  rule: {
    type: 'NUMBER_GREATER',
    values: [{ userEnteredValue: '1000' }],
  },
  format: {
    backgroundColor: { red: 0.7, green: 1, blue: 0.7 }, // Light green
  },
});

Marketplace Examples

Complete, copy-pasteable prompts for Claude Desktop or any MCP client.

Example 1: Sales Dashboard with XLOOKUP Formulas

"Create a sales dashboard in spreadsheet 1BxiMVs0... with XLOOKUP formulas that look up product names from a Products sheet and return their category and price."

// Step 1: Generate the XLOOKUP formula
{ "action": "generate_formula", "spreadsheetId": "1BxiMVs0...", "formulaType": "xlookup",
  "description": "Look up product name in Products!A:A and return the category from Products!C:C",
  "targetCell": "D2" }

// Step 2: Write the formula to the dashboard
{ "action": "write", "spreadsheetId": "1BxiMVs0...", "range": "Dashboard!D2",
  "values": [["=XLOOKUP(A2,Products!A:A,Products!C:C,\"Unknown\")"]] }

// Step 3: Format the dashboard
{ "action": "batch_format", "spreadsheetId": "1BxiMVs0...",
  "operations": [{ "range": "Dashboard!A1:E1", "preset": "header_row" }] }

Example 2: Quarterly Data Analysis with Charts

"Analyze my Q4 revenue data in sheet 'Q4 Data' and create a column chart comparing monthly revenue."

// Step 1: Scout the spreadsheet structure
{ "action": "scout", "spreadsheetId": "1BxiMVs0..." }

// Step 2: Comprehensive analysis
{ "action": "comprehensive", "spreadsheetId": "1BxiMVs0...", "range": "'Q4 Data'!A1:D100" }

// Step 3: Create the chart
{ "action": "suggest_chart", "spreadsheetId": "1BxiMVs0...", "range": "'Q4 Data'!A1:D13" }

Example 3: Import CSV, Deduplicate, and Format as Table

"Import this CSV of customer data, remove duplicates on the email column, and format it as a styled table."

// Step 1: Import the CSV
{ "action": "import_csv", "spreadsheetId": "1BxiMVs0...", "sheetName": "Customers",
  "csvData": "Name,Email,Revenue\nAlice,[email protected],5000\n..." }

// Step 2: Deduplicate on Email column
{ "action": "deduplicate", "spreadsheetId": "1BxiMVs0...", "range": "Customers!A1:C100",
  "keyColumns": ["Email"], "keep": "first" }

// Step 3: Create a table
{ "action": "create_table", "spreadsheetId": "1BxiMVs0...", "range": "Customers!A1:C50",
  "tableName": "CustomerTable", "hasHeaders": true }

Example 4: Automated Sharing and Version Snapshots

"Share my budget spreadsheet with the finance team, create a version snapshot before making changes, and track the edit."

// Step 1: Create a version snapshot before editing
{ "action": "version_create_snapshot", "spreadsheetId": "1BxiMVs0...",
  "name": "Pre-Q4-Budget-Edit", "description": "Snapshot before Q4 budget update" }

// Step 2: Poll until the snapshot task completes
{ "action": "version_snapshot_status", "spreadsheetId": "1BxiMVs0...",
  "taskId": "task_123" }

// Step 3: Share with the team
{ "action": "share_add", "spreadsheetId": "1BxiMVs0...",
  "emailAddress": "[email protected]", "role": "writer",
  "sendNotification": true, "emailMessage": "Q4 budget ready for review" }

// Step 4: Make changes, then create another snapshot
{ "action": "version_create_snapshot", "spreadsheetId": "1BxiMVs0...",
  "name": "Post-Q4-Budget-Edit" }

Example 5: Create Reusable Named Ranges And Protections

"Name my key financial ranges and protect the assumptions section before I share this model."

// Step 1: Name a key assumptions range
{ "action": "add_named_range", "spreadsheetId": "1BxiMVs0...",
  "name": "ASSUMPTIONS",
  "range": "Model!B2:D10" }

// Step 2: Protect it before collaboration
{ "action": "add_protected_range", "spreadsheetId": "1BxiMVs0...",
  "range": "Model!B2:D10",
  "description": "Locked financial assumptions",
  "warningOnly": false }

// Step 3: Add metadata for downstream automation
{ "action": "add_developer_metadata", "spreadsheetId": "1BxiMVs0...",
  "metadataKey": "section",
  "metadataValue": "financial_assumptions",
  "visibility": "DOCUMENT" }

Safety Rails

Dry Run Mode

Preview changes without executing:

{
  safety: {
    dryRun: true;
  }
}

Effect Scope Limits

Prevent accidental large-scale changes:

{
  safety: {
    effectScope: {
      maxCellsAffected: 5000,
      requireExplicitRange: true
    }
  }
}

Expected State (Optimistic Locking)

Ensure data hasn't changed since last read:

{
  safety: {
    expectedState: {
      rowCount: 100,
      sheetTitle: 'Sales Data',
      checksum: 'abc123'
    }
  }
}

Auto-Snapshot

Create backup before destructive operations:

{
  safety: {
    autoSnapshot: true;
  }
}

Range Input Formats

ServalSheets accepts multiple range formats:

// A1 notation
{ a1: "Sheet1!A1:C10" }

// Named range
{ namedRange: "SalesData" }

// Grid coordinates (0-based, end exclusive)
{ grid: { sheetId: 0, startRowIndex: 0, endRowIndex: 10, startColumnIndex: 0, endColumnIndex: 3 } }

// Semantic (header-based)
{ semantic: { sheet: "Sales", column: "Revenue", includeHeader: false } }

Error Codes

Code	Description	Retryable
`PARSE_ERROR`	Invalid JSON	No
`INVALID_PARAMS`	Invalid parameters	No
`SHEET_NOT_FOUND`	Sheet doesn't exist	No
`RANGE_NOT_FOUND`	Range not found	No
`PERMISSION_DENIED`	No access	No
`QUOTA_EXCEEDED`	API quota exceeded	Yes
`RATE_LIMITED`	Too many requests	Yes
`PRECONDITION_FAILED`	Expected state mismatch	No
`EFFECT_SCOPE_EXCEEDED`	Operation too large	No
`AMBIGUOUS_RANGE`	Multiple header matches	No

Authentication

Service Account (Recommended for automation)

export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
npx servalsheets

OAuth2 Access Token

export GOOGLE_ACCESS_TOKEN=ya29.xxx
npx servalsheets

OAuth2 Client Credentials

export GOOGLE_CLIENT_ID=xxx
export GOOGLE_CLIENT_SECRET=xxx
npx servalsheets

Encrypted Token Store (Optional)

Persist OAuth tokens across restarts using an encrypted file store.

export GOOGLE_TOKEN_STORE_PATH=~/.config/servalsheets/tokens.enc
export ENCRYPTION_KEY=<64-char-hex-key>
npx servalsheets

The key must be a 64-character hex string (32 bytes). Example:

openssl rand -hex 32

Enterprise SSO (SAML 2.0)

For organizations using an identity provider (Okta, Azure AD, Google Workspace SAML, etc.), ServalSheets ships a built-in SAML 2.0 Service Provider. When configured, users authenticate via your IdP and receive a short-lived JWT for subsequent API requests.

# Required
SAML_ENTRY_POINT=https://your-idp.example.com/sso/saml
SAML_ISSUER=https://your-servalsheets.example.com
SAML_CERT=-----BEGIN CERTIFICATE-----\n...\n-----END CERTIFICATE-----
SAML_CALLBACK_URL=https://your-servalsheets.example.com/sso/callback

# Optional
SAML_PRIVATE_KEY=<your-pem-private-key>              # for signed requests
SAML_WANT_ASSERTIONS_SIGNED=true                    # default: true
SAML_SIGNATURE_ALGORITHM=sha256                     # default: sha256
SSO_JWT_TTL=3600                                    # token TTL in seconds (default: 1h)
SSO_ALLOWED_CLOCK_SKEW=300                          # clock skew tolerance in seconds

SSO routes registered automatically when SAML_ENTRY_POINT is set:

Route	Description
`GET /sso/login`	Redirects to IdP login page
`POST /sso/callback`	Receives SAML assertion, issues JWT
`GET /sso/metadata`	Serves SP metadata XML for IdP registration
`GET /sso/logout`	Initiates SLO (Single Log-Out)

The issued JWT carries scope='sso' and is accepted by the same Bearer-token middleware as OAuth tokens. No client changes required — just swap the token.

Transport Security Model (RBAC)

ServalSheets enforces role-based access control (RBAC) only on HTTP transport. STDIO transport (used by Claude Desktop and local CLI) trusts the local process by design — it runs under the user's account with their OS-level permissions, so an additional RBAC layer would be redundant.

Transport	RBAC enforced?	Notes
STDIO	No	Trusted local process (Claude Desktop model)
HTTP / Streamable HTTP	Yes	JWT-based RBAC, configurable roles via `SERVAL_RBAC_*` env vars
Remote MCP	Yes	Per-user JWT claims validated on each request

If you are running ServalSheets as an HTTP server exposed to multiple users, ensure JWT_SECRET and OAUTH_CLIENT_SECRET are set and all traffic goes through HTTPS.

Configuration

ServalSheets supports extensive configuration via environment variables for production deployments.

Required for Production (HTTP/Remote Server)

When running ServalSheets as an HTTP or remote server with OAuth support, these environment variables are required in production mode:

# Required Production Secrets (generate with: openssl rand -hex 32)
export JWT_SECRET=<64-char-hex-string>
export STATE_SECRET=<64-char-hex-string>
export OAUTH_CLIENT_SECRET=<64-char-hex-string>

# OAuth Security (comma-separated list of allowed callback URIs)
export ALLOWED_REDIRECT_URIS=https://your-app.com/callback,https://another-app.com/callback

# Environment mode
export NODE_ENV=production

Security Notes:

Each secret should be a unique 64-character hex string
Never commit secrets to version control
Rotate secrets every 90 days
Use a secrets manager in production (AWS Secrets Manager, Vault, etc.)

See SECURITY.md for detailed security best practices.

Optional: Redis for Session Storage

For production deployments with multiple instances or high availability requirements:

# Install Redis dependency
npm install redis

# Configure Redis URL
export REDIS_URL=redis://localhost:6379

# Optional: Maximum sessions per user (default: 5)
export MAX_SESSIONS_PER_USER=5

# Optional: Streamable HTTP event store (resumability)
export STREAMABLE_HTTP_EVENT_TTL_MS=300000
export STREAMABLE_HTTP_EVENT_MAX_EVENTS=5000

Redis provides:

Shared session storage across multiple server instances
Persistent sessions across server restarts
Streamable HTTP resumability across instances
Better performance at scale

Rate Limiting

Control API quota usage with token bucket rate limiting:

# Configure rate limits (default: 300 reads/min, 60 writes/min)
export RATE_LIMIT_READS_PER_MINUTE=300
export RATE_LIMIT_WRITES_PER_MINUTE=60

Google Sheets API Quotas by Workspace Edition:

Workspace Edition	Read Quota	Write Quota	Configuration
Free/Personal	300/min	60/min	(default values)
Business Standard	600/min	120/min	`RATE_LIMIT_READS_PER_MINUTE=600 RATE_LIMIT_WRITES_PER_MINUTE=120`
Business Plus	900/min	180/min	`RATE_LIMIT_READS_PER_MINUTE=900 RATE_LIMIT_WRITES_PER_MINUTE=180`
Enterprise	1200/min	240/min	`RATE_LIMIT_READS_PER_MINUTE=1200 RATE_LIMIT_WRITES_PER_MINUTE=240`

Note: Actual quotas depend on your Google Cloud project configuration. Check your Google Cloud Console for exact limits.

Dynamic Throttling: When a 429 (rate limit) error is detected, the rate limiter automatically reduces rates by 50% for 60 seconds, then restores normal limits.

Adjust these based on your Google Cloud project quotas. See PERFORMANCE.md for tuning strategies.

Caching

Configure cache TTLs and sizes:

# Enable/disable caching (default: enabled)
export CACHE_ENABLED=true

# Cache configuration
export CACHE_MAX_SIZE_MB=100
export CACHE_TTL_MS=300000  # 5 minutes

Caching reduces API calls by 100x for repeated reads. Increase TTLs for read-heavy workloads.

Observability

Configure tracing and monitoring:

# Enable OpenTelemetry tracing
export OTEL_ENABLED=true
export OTEL_LOG_SPANS=true  # Log spans to console

# Request deduplication
export DEDUPLICATION_ENABLED=true
export DEDUP_WINDOW_MS=5000  # 5 seconds

Automatic Monitoring:

Payload sizes: All Google API requests/responses tracked
Batch efficiency: Intents per spreadsheet ratio analyzed
Rate limiting: Dynamic throttling on 429 errors
HTTP compression: Response sizes reduced by 60-80%

Statistics available via lifecycle methods:

getCacheStats() - Cache hit rates, sizes
getDeduplicationStats() - Deduplication rates
getBatchEfficiencyStats() - Batch optimization metrics
getTracingStats() - OpenTelemetry span counts

Effect Scope Limits

Prevent accidental large-scale operations:

Effect-scope safety rails use built-in defaults in the current server:

Estimated cells per operation default limit: 50,000
Destructive row deletes default limit: 10,000
Destructive column deletes default limit: 100

You can tighten limits per request with effectScope, especially maxCellsAffected and requireExplicitRange.

These limits act as safety rails. Operations exceeding limits will fail with EFFECT_SCOPE_EXCEEDED error.

Logging

Configure structured logging:

# Log level: debug, info, warn, error (default: info)
export LOG_LEVEL=info

# Log format: json, text (default: json for production)
export LOG_FORMAT=json

# Log file path (optional, defaults to stdout)
export LOG_FILE=/var/log/servalsheets/app.log

JSON format is recommended for production (machine-parseable). See MONITORING.md for log aggregation.

Timeouts

Configure API and request timeouts:

# Google API timeout (default: 30s)
export GOOGLE_API_TIMEOUT_MS=30000

# Request timeout (default: 120s)
export REQUEST_TIMEOUT_MS=120000

HTTP/2 and Connection Pool

Configure HTTP/2 and connection pooling for optimal performance:

# Enable/disable HTTP/2 (default: true)
export GOOGLE_API_HTTP2_ENABLED=true

# Maximum concurrent connections (default: 50)
export GOOGLE_API_MAX_SOCKETS=50

# Keep-alive timeout in milliseconds (default: 30000)
export GOOGLE_API_KEEPALIVE_TIMEOUT=30000

# Enable connection pool monitoring (default: false)
export ENABLE_HTTP2_POOL_MONITORING=true

# Monitoring interval in milliseconds (default: 300000 = 5 minutes)
export HTTP2_POOL_MONITOR_INTERVAL_MS=300000

Benefits of HTTP/2:

5-15% latency reduction for API calls
Connection multiplexing (multiple requests per connection)
Header compression reduces overhead

Connection Pool Monitoring: When enabled, logs connection pool statistics at regular intervals:

Active sockets (in-use connections)
Free sockets (available in pool)
Pending requests (waiting for connection)
Pool utilization percentage
Automatic warnings at 80% and 100% utilization

Recommended for production to detect connection pool exhaustion before it impacts performance.

Metrics Server

Expose performance metrics via HTTP endpoint for monitoring:

# Enable metrics server (default: false)
export ENABLE_METRICS_SERVER=true

# Metrics server port (default: 9090)
export METRICS_PORT=9090

# Metrics server host (default: 127.0.0.1)
export METRICS_HOST=127.0.0.1

Available endpoints:

Endpoint	Format	Description
`/metrics`	Prometheus text	Recommended for Prometheus/Grafana
`/metrics.json`	JSON	Programmatic access
`/metrics.txt`	Human-readable text	Quick inspection
`/health`	JSON	Health check endpoint

Metrics exposed:

Cache metrics: Hit rate, hits/misses, evictions, size by cache type
Batching metrics: Current window size, total batches, average batch size, deduplication count
API metrics: Total calls by method, errors by code, success/error rates

Example Prometheus configuration:

scrape_configs:
  - job_name: 'servalsheets'
    static_configs:
      - targets: ['localhost:9090']
    scrape_interval: 15s

Access metrics:

# Prometheus format
curl http://localhost:9090/metrics

# JSON format
curl http://localhost:9090/metrics.json

# Human-readable
curl http://localhost:9090/metrics.txt

Memory Leak Detection

Monitor Node.js heap usage to detect memory leaks before they cause crashes:

# Enable heap monitoring (default: false)
export ENABLE_HEAP_MONITORING=true

# Monitoring interval in milliseconds (default: 1800000 = 30 minutes)
export HEAP_MONITOR_INTERVAL_MS=1800000

# Warning threshold (0-1, default: 0.7 = 70%)
export HEAP_WARNING_THRESHOLD=0.7

# Critical threshold (0-1, default: 0.85 = 85%)
export HEAP_CRITICAL_THRESHOLD=0.85

# Enable heap snapshots at critical threshold (default: false)
export ENABLE_HEAP_SNAPSHOTS=true

# Heap snapshot directory (default: ./heap-snapshots)
export HEAP_SNAPSHOT_PATH=./heap-snapshots

Alerting thresholds:

Warning (70%): Logs elevated heap usage, monitor for sustained growth
Critical (85%): Logs error with recommendation, optionally captures heap snapshot
Automatic throttling: Alerts limited to once every 5-15 minutes to prevent spam

Heap snapshots: When enabled, heap snapshots are captured at critical threshold for post-mortem analysis:

Chrome DevTools: Open snapshot in Memory Profiler
clinic.js: npm run profile:memory uses npm exec to fetch Clinic.js on demand

Recommendations by utilization:

95%+: Immediate restart required to prevent OOM crash
85-95%: Schedule restart, investigate with heap snapshots
70-85%: Monitor trends, review cache policies and connection pools
<70%: Normal operation

Example: Production Configuration

Complete production setup for Claude Desktop:

{
  "mcpServers": {
    "servalsheets": {
      "command": "npx",
      "args": ["servalsheets"],
      "env": {
        "NODE_ENV": "production",
        "LOG_LEVEL": "info",
        "LOG_FORMAT": "json",

        "GOOGLE_APPLICATION_CREDENTIALS": "/path/to/service-account.json",
        "GOOGLE_TOKEN_STORE_PATH": "/path/to/tokens.enc",
        "ENCRYPTION_KEY": "<64-char-hex-key>"
      }
    }
  }
}

For detailed configuration guides, see:

Security: SECURITY.md - Authentication, encryption, secrets management
Performance: PERFORMANCE.md - Rate limiting strategies, diff tiers, batching
Monitoring: MONITORING.md - Logging, metrics, alerting, health checks
Deployment: DEPLOYMENT.md - Docker, Kubernetes, systemd, cloud platforms
Troubleshooting: TROUBLESHOOTING.md - Common issues and solutions

Development

# Clone repository
git clone https://github.com/khill1269/servalsheets.git
cd servalsheets

# Install dependencies
npm install

# Build
npm run build

# Type check (strict mode)
npm run typecheck

# Run tests (8,500+ tests)
npm test

# Run in development mode
npm run dev

# Start HTTP server
npm run start:http

# Start remote server with OAuth
npm run start:remote

Architecture

src/
├── schemas/           # Zod schemas for all 25 tools
├── core/              # Core infrastructure
│   ├── intent.ts          # Intent types and mappings
│   ├── batch-compiler.ts  # Compiles intents to API requests
│   ├── rate-limiter.ts    # Token bucket rate limiting
│   ├── diff-engine.ts     # Tiered diff generation
│   ├── policy-enforcer.ts # Safety policy validation
│   └── range-resolver.ts  # Semantic range resolution
├── services/          # External service integrations
│   ├── google-api.ts      # Google API client
│   └── snapshot.ts        # Backup/restore service
├── handlers/          # Tool handlers
├── server.ts          # MCP server (STDIO)
├── http-server.ts     # Streamable HTTP transport
├── oauth-provider.ts  # OAuth 2.1 for Claude Connectors
├── cli.ts             # CLI entry point
└── index.ts           # Main exports

Data Flow

graph LR
    A[User] -->|Natural language| B[Claude Desktop]
    B -->|MCP Protocol| C[ServalSheets MCP Server]
    C -->|Google API v4| D[Google Sheets]
    D -->|Data| C
    C -->|Structured response| B
    B -->|AI response| A

    style A fill:#e1f5ff
    style B fill:#fff3cd
    style C fill:#d4edda
    style D fill:#f8d7da

Component Architecture

graph TB
    subgraph "ServalSheets MCP Server"
        CLI[CLI Entry Point]
        MCP[MCP Server]

        subgraph "Handlers (25 Tools)"
            H1[sheets_core]
            H2[sheets_data]
            H3[sheets_format]
            H4[... 22 more]
        end

        subgraph "Core Infrastructure"
            Intent[Intent System]
            Compiler[Batch Compiler]
            RateLimit[Rate Limiter]
            Diff[Diff Engine]
            Policy[Policy Enforcer]
            Range[Range Resolver]
        end

        subgraph "Services"
            GoogleAPI[Google API Client]
            Snapshot[Snapshot Service]
        end

        CLI --> MCP
        MCP --> H1 & H2 & H3 & H4
        H1 & H2 & H3 & H4 --> Intent
        Intent --> Compiler
        Compiler --> Policy
        Policy --> RateLimit
        RateLimit --> GoogleAPI
        GoogleAPI --> Diff
        Diff --> Range
        Range --> Snapshot
    end

    GoogleAPI -->|API Calls| Google[Google Sheets API]

    style CLI fill:#e1f5ff
    style MCP fill:#fff3cd
    style GoogleAPI fill:#d4edda
    style Google fill:#f8d7da

Protocol Compliance

MCP Protocol: 2025-11-25 (discriminated unions, progress notifications)
Google Sheets API: v4 (full coverage)
OAuth: 2.1 with PKCE support
Transports: STDIO, Streamable HTTP, legacy SSE compatibility
TypeScript: Strict mode enabled, 0 errors
SDK Version: @modelcontextprotocol/[email protected]
Test Suite: 8,500+ passing tests across unit, integration, contract, and protocol suites

Quality Metrics

✅ Type Safety: Full TypeScript strict mode compliance
✅ Test Suite: 8,500+ passing tests with CI coverage reporting
✅ Protocol Compliance: MCP 2025-11-25 certified
✅ Production Ready: Hardened for hosted deployments and submission-oriented remote connector flows
✅ Error Handling: Comprehensive error codes with retry hints

Schema Architecture: Discriminated Unions

ServalSheets uses Zod discriminated unions for type-safe action dispatch across 25 tools and 410 actions. This architecture provides:

Pattern Overview

Each tool defines a discriminated union schema where the action field serves as the discriminator:

// Example: sheets_auth tool (4 actions)
const SheetsAuthInputSchema = z.object({
  request: z.discriminatedUnion('action', [
    z.object({ action: z.literal('status'), verbosity: VerbositySchema }),
    z.object({ action: z.literal('login'), scopes: ScopesSchema, verbosity: VerbositySchema }),
    z.object({ action: z.literal('callback'), code: z.string(), verbosity: VerbositySchema }),
    z.object({ action: z.literal('logout'), verbosity: VerbositySchema }),
  ]),
});

Benefits

Type Safety: TypeScript compiler ensures all action variants are handled
Zero Runtime Overhead: Discriminated unions compile to efficient JavaScript
Exhaustiveness Checking: Handlers must implement all action types
Zod v4 Native JSON Schema: Direct OpenAPI/MCP schema generation
Performance: 14x faster string parsing, 7x faster arrays (Zod 4.3.5)

Tool Structure

All 25 tools follow this pattern:

Tool: sheets_[category]
├─ Input: Discriminated union of action variants
├─ Output: Success/Error discriminated union
├─ Type Narrowing: Auto-generated type guards per action
└─ Handlers: Single method per action variant

Example Action Variants (sheets_data):

Action	Input Shape	Output	Use Case
`read`	`{ action: 'read', spreadsheetId, range, valueRenderOption }`	Values array	Fetch cell data
`write`	`{ action: 'write', spreadsheetId, range, values, safety }`	Write summary	Update cells
`batch_read`	`{ action: 'batch_read', spreadsheetId, ranges }`	Multi-range values	Fetch multiple ranges
`batch_write`	`{ action: 'batch_write', spreadsheetId, data }`	Batch summary	Multi-range update
`append`	`{ action: 'append', spreadsheetId, range, values }`	Append summary	Add rows
`clear`	`{ action: 'clear', spreadsheetId, range }`	Clear summary	Delete values (keep format)

Response Format

All tool responses use a discriminated union by success field:

// Success response
{
  success: true,
  action: 'read',
  values: [[...cell values...]],
  _meta: { requestId, duration, cacheHit, ... }
}

// Error response
{
  success: false,
  error: {
    code: 'QUOTA_EXCEEDED',
    message: '...',
    retryable: true,
    retryAfterMs: 60000,
    resolution: '...',
    resolutionSteps: ['...']
  }
}

25 Tools Overview

Tool	Actions	Pattern	Use Case
`sheets_auth`	4	Status, Login, Callback, Logout	OAuth & credentials
`sheets_core`	19	Get, Create, Delete, List, Update	Sheet metadata
`sheets_data`	19	Read, Write, Append, Clear, Batch ops	Cell values & notes
`sheets_format`	23	Colors, Borders, Validation, Conditionals	Styling & rules
`sheets_dimensions`	28	Insert, Delete, Resize, Filter, Sort, Freeze	Rows & columns
`sheets_visualize`	18	Create, Update charts, Pivot tables	Charts & pivots
`sheets_collaborate`	35	Share, Comments, Versions, Snapshots	Multi-user features
`sheets_advanced`	31	Named ranges, Protected ranges, Metadata, Banding	Advanced features
`sheets_transaction`	6	Begin, Queue, Commit, Rollback	Atomic operations
`sheets_quality`	4	Validate, Detect conflicts, Impact analysis	Data quality
`sheets_history`	7	Undo, Redo, Revert, List history	Version control
`sheets_confirm`	5	Request, Wizard, Elicitation	User confirmations
`sheets_analyze`	18	Comprehensive, Scout, Planner, Suggestions	AI analysis
`sheets_fix`	6	Fix, Clean, Standardize, Fill, Anomalies, Suggest	Data cleaning pipeline
`sheets_composite`	14	Import CSV, Deduplicate, Generate sheet	Bulk ops & generation
`sheets_session`	26	Set active, Get context, Save checkpoint	Session context
`sheets_appsscript`	18	Run, Deploy, Get content	Apps Script automation
`sheets_bigquery`	17	Query, Import, Connect Looker	BigQuery integration
`sheets_templates`	8	List, Create, Apply, Import builtin	Templates
`sheets_webhook`	7	Register, Unregister, List, Test	Change notifications
`sheets_dependencies`	7	Build, Analyze, Detect cycles, Export	Formula analysis

Error Handling: Recovery Strategies

ServalSheets implements a comprehensive error classification system with recovery strategies for each error type. All errors inherit from ServalSheetsError and provide actionable resolution steps.

Error Classes and Recovery

1. ValidationError (Non-Retryable)

When: Invalid input, malformed data, type mismatches

Recovery Strategy:

// Error details always include field name and expected format
{
  code: 'VALIDATION_ERROR',
  message: 'Invalid spreadsheetId format',
  field: 'spreadsheetId',
  expectedFormat: 'String matching /^[a-zA-Z0-9-_]{44}$/',
  retryable: false,
  resolution: "Fix the value of 'spreadsheetId' and retry the operation.",
  resolutionSteps: [
    "1. Check the value of 'spreadsheetId'",
    "2. Ensure it matches the required format",
    "3. Expected format: String matching /^[a-zA-Z0-9-_]{44}$/"
  ]
}

Action: Fix input and retry immediately (no backoff needed)

2. AuthenticationError (Retryable)

When: Token expired, invalid credentials, auth flow failures

Recovery Strategy:

// Retryable auth errors include refresh instructions
{
  code: 'TOKEN_EXPIRED',
  message: 'Access token expired',
  retryable: true,
  resolution: 'Re-authenticate and retry the operation.',
  resolutionSteps: [
    '1. Refresh your access token',
    '2. Re-authenticate if refresh fails',
    '3. Retry the operation'
  ]
}

Action:

Attempt token refresh
If refresh fails, restart OAuth flow
Retry operation with new token
Use exponential backoff (1s, 2s, 4s, 8s)

3. QuotaExceededError (Retryable with Smart Backoff)

When: API quota exhausted, rate limited (429 errors)

Recovery Strategy:

{
  code: 'QUOTA_EXCEEDED',
  message: 'Read quota exceeded (300 reads/min)',
  retryable: true,
  retryAfterMs: 60000,           // Wait 60 seconds before retry
  retryStrategy: 'exponential_backoff',
  quotaType: 'read',             // read | write | requests | unknown
  resetTime: '2026-02-05T18:05:00Z',
  resolution: 'Wait 60 seconds, then retry with optimized batch operations',
  resolutionSteps: [
    '1. Wait 60 seconds before retrying (quota resets at 2026-02-05T18:05:00Z)',
    '2. Optimize future requests:',
    '   - Use batch operations: sheets_data action="batch_read" (saves ~80% quota)',
    '   - Use transactions: sheets_transaction (batches 10+ ops into 1 API call)',
    '   - Enable caching for repeated reads',
    '3. Increase quotas in Google Cloud Console'
  ]
}

Action:

Wait retryAfterMs (usually 60 seconds)
Use batch operations to reduce future quota usage
Enable caching (see Performance Tuning section)
Implement request deduplication
Request higher quotas from Google Cloud Console

Optimization (80-90% quota savings):

// Before: 3 separate API calls = 3 quota units
await sheets_data({ action: 'read', range: 'A1:A100' });
await sheets_data({ action: 'read', range: 'B1:B100' });
await sheets_data({ action: 'read', range: 'C1:C100' });

// After: 1 batch API call = 1 quota unit (saves 66% quota)
await sheets_data({
  action: 'batch_read',
  ranges: ['A1:A100', 'B1:B100', 'C1:C100'],
});

4. SyncError (Retryable with Conflict Resolution)

When: Concurrent modifications, merge conflicts, stale data

Recovery Strategy (4 conflict types):

A) Concurrent Modification:

{
  code: 'TRANSACTION_CONFLICT',
  conflictType: 'concurrent_modification',
  message: 'Spreadsheet was edited by another user',
  retryable: true,
  resolutionSteps: [
    '1. Fetch latest state: sheets_core action="get"',
    '2. Apply your changes to the latest version',
    '3. Use transactions for atomic updates: sheets_transaction',
    '4. Lock ranges during edit: sheets_advanced action="add_protected_range"'
  ]
}

B) Stale Data (cached version outdated):

{
  conflictType: 'stale_data',
  lastKnownVersion: 42,
  currentVersion: 45,
  resolutionSteps: [
    '1. Fetch fresh data: sheets_core action="get"',
    '2. Invalidate local cache',
    '3. Use webhooks instead of polling: sheets_webhook',
    '4. Set shorter cache TTL'
  ]
}

C) Version Mismatch:

{
  conflictType: 'version_mismatch',
  resolutionSteps: [
    '1. Get current version: sheets_core action="get"',
    '2. Check version history: sheets_collaborate action="version_list"',
    '3. Use sheets_transaction for multi-step updates'
  ]
}

D) Merge Conflict (manual resolution required):

{
  conflictType: 'merge_conflict',
  resolutionSteps: [
    '1. View current state: sheets_core action="get"',
    '2. Implement resolution strategy:',
    '   - Last-write-wins: Use latest timestamp',
    '   - First-write-wins: Keep original version',
    '   - Three-way merge: Compare base, yours, theirs',
    '   - Custom: Use app-specific merge logic'
  ]
}

5. ApiTimeoutError (Retryable with Optimization)

When: Request exceeds timeout (default 30s), slow network

Recovery Strategy:

{
  code: 'DEADLINE_EXCEEDED',
  retryable: true,
  timeoutMs: 30000,
  operation: 'batch_read_large_range',
  resolutionSteps: [
    '1. Reduce request size by limiting rows/columns',
    '2. Split into smaller batches',
    '3. Use batch operations instead of individual requests',
    '4. Disable formula recalculation if possible',
    '5. Increase timeout setting',
    '6. Verify network connection is stable'
  ],
  retryAfterMs: 60000,
  retryStrategy: 'exponential_backoff'
}

Action:

Reduce request scope (fewer rows/columns)
Implement pagination
Use batch operations
Cache intermediate results
Retry with exponential backoff

6. RangeResolutionError (Non-Retryable)

When: Invalid range format, sheet not found, ambiguous column names

Recovery Strategy (context-specific):

Invalid Range Format:

{
  code: 'INVALID_RANGE',
  rangeInput: 'Sheet1!A1:B',  // Invalid (missing end row)
  resolutionSteps: [
    '1. Check A1 notation format: Use "Sheet1!A1:D10" or "A1:D10"',
    '2. Valid examples: "Sheet1!A1", "Sheet1!A:A" (column), "Sheet1!1:1" (row)',
    '3. Escape sheet names with spaces: "\'My Sheet\'!A1:B10"',
    '4. Verify cell coordinates are valid (column A-ZZZ, rows 1-10000000)',
    '5. Try semantic range syntax: {"semantic":{"sheet":"Sales","column":"Revenue"}}'
  ]
}

Sheet Not Found:

{
  code: 'SHEET_NOT_FOUND',
  sheetName: 'NonexistentSheet',
  resolutionSteps: [
    '1. List all sheets: sheets_core action="list_sheets"',
    '2. Sheet requested: "NonexistentSheet" (case-sensitive)',
    '3. Verify sheet name spelling exactly as shown in Google Sheets',
    '4. Try using sheet ID (numeric gid) instead of name'
  ]
}

Ambiguous Column Name:

{
  code: 'AMBIGUOUS_RANGE',
  resolutionSteps: [
    '1. Multiple columns match your query',
    '2. Specify exact column name or use A1 notation instead',
    '3. Use sheets_core to see all available columns'
  ]
}

7. BatchCompilationError (Non-Retryable)

When: Multiple operations fail validation, circular references, schema mismatches

Recovery Strategy:

{
  code: 'BATCH_UPDATE_ERROR',
  failedOperations: [
    { index: 2, error: 'Invalid range format' },
    { index: 5, error: 'Circular reference detected' },
    { index: 8, error: 'Protected range violation' }
  ],
  failureRate: '15.0%',
  resolutionSteps: [
    '1. 3 failed operations out of 20 (15.0%)',
    '2. Common issues:',
    '   - Invalid range format in operation (use "Sheet1!A1:B10")',
    '   - Sheet name mismatch (case-sensitive)',
    '   - Circular reference in formulas',
    '   - Protected ranges or sheets',
    '3. Fix each failed operation:',
    '   - Operation 2: Invalid range format',
    '   - Operation 5: Circular reference detected',
    '4. Split into smaller batches if needed (max 50 ops recommended)',
    '5. Use sheets_transaction with corrected operations'
  ]
}

Action:

Review each failed operation
Fix validation errors
Split into smaller batches (50 ops max)
Use transactions for atomicity
Retry corrected batch

Retry Logic Best Practices

// Exponential backoff with jitter
async function retryWithBackoff(operation: () => Promise<T>, maxRetries: number = 5): Promise<T> {
  for (let attempt = 0; attempt <= maxRetries; attempt++) {
    try {
      return await operation();
    } catch (error) {
      // Check if retryable
      if (!error.retryable || attempt === maxRetries) {
        throw error; // Non-retryable or final attempt
      }

      // Calculate backoff with jitter
      const baseDelay = Math.pow(2, attempt) * 1000; // 1s, 2s, 4s, 8s, ...
      const jitter = Math.random() * 0.1 * baseDelay; // 10% jitter
      const delayMs = baseDelay + jitter;

      // Check if error specifies retry delay
      const specifiedDelay = error.retryAfterMs;
      const actualDelayMs = specifiedDelay || delayMs;

      console.log(
        `Attempt ${attempt + 1}/${maxRetries + 1} failed. ` +
          `Waiting ${(actualDelayMs / 1000).toFixed(1)}s before retry...`
      );

      await new Promise((resolve) => setTimeout(resolve, actualDelayMs));
    }
  }
}

Error Monitoring

Enable error tracking via metrics:

// Track errors by code and retryability
interface ErrorMetrics {
  totalErrors: number;
  errorsByCode: Record<string, number>;
  retryableCount: number;
  nonRetryableCount: number;
  successRateAfterRetry: number;
}

Performance Tuning

ServalSheets offers multiple performance optimization strategies for different workloads. Performance depends on: request batching, caching, rate limits, and payload sizes.

1. Caching (80-100x API call reduction)

Configuration:

# Enable/disable caching (default: enabled)
export CACHE_ENABLED=true

# Cache size limit (default: 100MB)
export CACHE_MAX_SIZE_MB=100

# Cache TTL (default: 5 minutes = 300000ms)
export CACHE_TTL_MS=300000

# Cache cleanup interval (default: 5 minutes)
export CACHE_CLEANUP_INTERVAL_MS=300000

Cache Strategy by Workload:

A) Read-Heavy Workloads (same data accessed repeatedly):

# Increase TTL to 30 minutes for stable data
export CACHE_TTL_MS=1800000
export CACHE_MAX_SIZE_MB=500  # Larger cache

# Example: Dashboard refreshing every 5 minutes
# First load: 10 API calls
# Refreshes 2-5: 0 API calls (cached)
# Result: 95% quota savings

B) Real-Time Workloads (data changes frequently):

# Disable caching for absolutely fresh data
export CACHE_ENABLED=false

# OR use webhook-based notifications instead of caching
# This is more efficient than polling with short TTLs

C) Hybrid Approach (recommended):

# Short TTL for frequently-changing data
export CACHE_TTL_MS=30000  # 30 seconds

# Use webhooks for critical updates
# Use caching for non-critical metadata

# Example: Real-time metrics with stable schema
# Metrics cache expires every 30s → fresh data
# Schema cache expires every 5 min → reduces overhead

Cache Stats Monitoring:

// Access cache statistics
const stats = cacheManager.getStats();
// {
//   totalEntries: 1024,
//   totalSize: 52000000,  // 52MB
//   hits: 4850,
//   misses: 250,
//   hitRate: 0.951,       // 95.1% hit rate
//   byNamespace: {
//     'metadata': 512,
//     'values': 512
//   }
// }

2. Batch Operations (80-90% quota savings)

Pattern: Combine multiple operations into single API call

Read Batching (Quota efficiency):

// ❌ Inefficient: 3 API calls = 3 quota units
const range1 = await sheets_data({ action: 'read', range: 'Sales!A1:A100' });
const range2 = await sheets_data({ action: 'read', range: 'Sales!B1:B100' });
const range3 = await sheets_data({ action: 'read', range: 'Sales!C1:C100' });

// ✅ Efficient: 1 API call = 1 quota unit (66% quota savings)
const [range1, range2, range3] = await sheets_data({
  action: 'batch_read',
  ranges: ['Sales!A1:A100', 'Sales!B1:B100', 'Sales!C1:C100'],
});

Write Batching:

// ❌ Inefficient: 3 API calls = 3 quota units
await sheets_data({ action: 'write', range: 'Sheet1!A1:A100', values: dataA });
await sheets_data({ action: 'write', range: 'Sheet1!B1:B100', values: dataB });
await sheets_data({ action: 'write', range: 'Sheet1!C1:C100', values: dataC });

// ✅ Efficient: 1 API call = 1 quota unit
await sheets_data({
  action: 'batch_write',
  data: [
    { range: 'Sheet1!A1:A100', values: dataA },
    { range: 'Sheet1!B1:B100', values: dataB },
    { range: 'Sheet1!C1:C100', values: dataC },
  ],
});

Transaction Batching (10-50 operations per API call):

// Use transactions for complex multi-step operations
// Each transaction = 1 API call regardless of operation count
await sheets_transaction({
  action: 'begin',
  spreadsheetId: '...'
});

// Queue up to 50 operations
await sheets_transaction({
  action: 'queue',
  operations: [
    { type: 'insert_rows', ... },
    { type: 'format_cells', ... },
    { type: 'set_formulas', ... },
    // ... more operations
  ]
});

await sheets_transaction({
  action: 'commit'
});

// Result: 50+ operations in 1 API call

Quota Savings by Batch Size:

Strategy	API Calls	Quota Units	Savings
Individual ops (1 at a time)	100	100	0%
Batch read/write (10 ops/call)	10	10	90%
Transactions (50 ops/call)	2	2	98%
Batch + Cache (repeat reads)	1	1	99%

3. Rate Limiting (Prevent 429 errors)

Configuration:

# Configure per your Google Workspace edition
# Default: 300 reads/min, 60 writes/min (Free tier)

# Business Standard: 600 reads/min, 120 writes/min
export RATE_LIMIT_READS_PER_MINUTE=600
export RATE_LIMIT_WRITES_PER_MINUTE=120

# Business Plus: 900 reads/min, 180 writes/min
export RATE_LIMIT_READS_PER_MINUTE=900
export RATE_LIMIT_WRITES_PER_MINUTE=180

# Enterprise: 1200 reads/min, 240 writes/min
export RATE_LIMIT_READS_PER_MINUTE=1200
export RATE_LIMIT_WRITES_PER_MINUTE=240

Token Bucket Algorithm:

Tokens refill at your configured rate (per second)
Each operation consumes tokens
Operations wait if insufficient tokens (backpressure)
429 errors trigger automatic 50% rate reduction for 60s

Example: Free tier with batch optimization

// Configuration: 300 reads/min = 5 reads/sec

// Load test 30 spreadsheets worth of data
// Without batching: 30 ops * 10 fields = 300 reads
// Rate limit: 300 reads/min = 5 reads/sec
// Time needed: 300 / 5 = 60 seconds

// With batching: 3 batch_read calls (10 ranges each)
// Time needed: 3 / 5 = 0.6 seconds

// 100x faster with same quota!

Dynamic Throttling (Automatic on 429 errors):

Standard rate → 429 Error → Reduce 50% (6 months) → Gradual restore → Normal rate
300 reads/min → 150 reads/min (60s) → 225 → 300

4. Payload Monitoring (Prevent 10MB hard limits)

Configuration:

# Enable payload monitoring
export ENABLE_PAYLOAD_MONITORING=true

# Payload size warnings (default: 2MB warning, 10MB hard limit)
export PAYLOAD_WARNING_SIZE_MB=2
export PAYLOAD_MAX_SIZE_MB=10

Monitor payload sizes:

// Monitor request/response sizes
const metrics = googleApi.getPayloadMetrics();
// {
//   largestRequest: 1500000,  // 1.5MB
//   largestResponse: 2500000, // 2.5MB
//   averageRequestSize: 45000,
//   averageResponseSize: 120000,
//   requestsAbove2MB: 3,
//   requestsAbove10MB: 0
// }

Optimize large payloads:

// ❌ Inefficient: Single read of entire sheet (10K rows × 100 cols)
const allData = await sheets_data({
  action: 'read',
  range: 'Sheet1!A1:CV10000',
});

// ✅ Efficient: Paginated reads
const pageSize = 100;
for (let page = 0; page < 100; page++) {
  const startRow = 1 + page * pageSize;
  const endRow = startRow + pageSize - 1;
  const pageData = await sheets_data({
    action: 'read',
    range: `Sheet1!A${startRow}:CV${endRow}`,
  });
  processPage(pageData);
}

5. Connection Pooling (HTTP/2)

Configuration:

# Enable HTTP/2 connection pooling (default: enabled)
export GOOGLE_API_HTTP2_ENABLED=true

# Max concurrent connections (default: 50)
export GOOGLE_API_MAX_SOCKETS=50

# Keep-alive timeout (default: 30 seconds)
export GOOGLE_API_KEEPALIVE_TIMEOUT=30000

# Enable pool monitoring
export ENABLE_HTTP2_POOL_MONITORING=true
export HTTP2_POOL_MONITOR_INTERVAL_MS=300000  # 5 minutes

Benefits:

5-15% latency reduction
Connection multiplexing (multiple requests per connection)
Header compression
Reduced TCP handshake overhead

Monitor pool health:

// Automatic warnings logged every 5 minutes (if enabled)
// Example log output:
// [INFO] HTTP/2 Pool Status: 45 active, 5 free, 0 pending
// [WARN] HTTP/2 Pool: 90% utilization - consider increasing GOOGLE_API_MAX_SOCKETS

6. Metrics & Observability

Configuration:

# Enable metrics server (default: disabled)
export ENABLE_METRICS_SERVER=true

# Metrics server port (default: 9090)
export METRICS_PORT=9090

# Enable tracing
export OTEL_ENABLED=true
export OTEL_LOG_SPANS=true

Access metrics:

# Prometheus format
curl http://localhost:9090/metrics

# JSON format
curl http://localhost:9090/metrics.json

# Human-readable
curl http://localhost:9090/metrics.txt

# Health check
curl http://localhost:9090/health

Key metrics to monitor:

Metric	Target	Action if High
Cache hit rate	>90%	Good! Cache is working
Cache hit rate	<50%	Increase TTL or max size
Avg batch size	>10 ops	Good! Operations batched
Avg batch size	<5 ops	Add more operations per batch
API errors 429	0/min	Good! Rate limit OK
API errors 429	>1/min	Reduce requests or increase quotas
Response time p99	<2s	Good! Performance OK
Response time p99	>10s	Check payload sizes, add caching

7. Timeout Configuration

Configuration:

# Google Sheets API timeout (default: 30 seconds)
export GOOGLE_API_TIMEOUT_MS=30000

# Request timeout (default: 120 seconds)
export REQUEST_TIMEOUT_MS=120000

Timeout tuning strategy:

Scenario	API Timeout	Request Timeout	Rationale
Large payloads (>5MB)	60000ms	120000ms	More time for transfer
Complex formulas	45000ms	90000ms	Formulas recalc slower
Standard operations	30000ms	60000ms	Default (recommended)
Latency-sensitive	20000ms	40000ms	Fail fast, retry quickly

Performance Tuning Checklist

[ ] Enable caching (CACHE_ENABLED=true, CACHE_TTL_MS=300000)
[ ] Use batch operations (batch_read, batch_write, transactions)
[ ] Configure rate limits per your quota (RATE_LIMIT_READS_PER_MINUTE)
[ ] Monitor payloads (<2MB typical, <10MB max)
[ ] Enable HTTP/2 (GOOGLE_API_HTTP2_ENABLED=true)
[ ] Set up metrics server (ENABLE_METRICS_SERVER=true)
[ ] Monitor cache hit rate (target: >90%)
[ ] Implement pagination for large datasets (>10K rows)
[ ] Use webhooks instead of polling (sheets_webhook)
[ ] Enable heap monitoring for long-running servers (ENABLE_HEAP_MONITORING=true)
[ ] Review slowest requests in metrics (/metrics endpoint)
[ ] Profile connection pool usage (HTTP2_POOL_MONITOR_INTERVAL_MS)
[ ] Test with your actual data volume
[ ] Measure baseline performance before optimizing
[ ] Monitor production metrics continuously

MCP Compliance Matrix

ServalSheets implements the MCP 2025-11-25 server features it advertises in discovery metadata. The matrix below summarizes the current MCP surface.

Protocol Coverage

Feature	Status	Version	Implementation
JSON-RPC 2.0	✅ Full	2.0	@modelcontextprotocol/sdk v1.29.0
Tools	✅ Full	2025-11-25	25 tools, 410 actions, discriminated unions
Resources	✅ Full	2025-11-25	56 MCP resources + 12 resource templates
Prompts	✅ Full	2025-11-25	40 guided workflows with arguments
Completions	✅ Full	2025-11-25	Argument autocompletion
Tasks	✅ Full	SEP-1686	Background execution, cancellation
Elicitation	✅ Full	SEP-1036	User confirmations for destructive ops
Sampling	✅ Full	SEP-1577	AI-powered analysis (sheets_analyze)
Logging	✅ Full	2025-11-25	Dynamic log level control
Progress	✅ Full	2025-11-25	Long-running operations reporting
Streaming	✅ Full	2025-11-25	Streamable HTTP + paginated responses

Feature Checklist

Tools (25 tools ✅)

All 25 tools are implemented and exercised in the test suite. See the Tool Summary above for current per-tool action counts.

Discriminated Union Schema ✅:

Input: z.discriminatedUnion('action', [...])
Output: z.discriminatedUnion('success', [...])
Type-safe handler dispatch
Exhaustiveness checking at compile time
Runtime validation via Zod

Resources (56 MCP resources + 12 templates ✅)

Implemented & Tested:

✅ sheets:///{spreadsheetId}
   └─ Spreadsheet metadata (title, sheets, properties)

✅ sheets:///{spreadsheetId}/{range}
   └─ Range values with formatting context

✅ sheets:///{spreadsheetId}/charts
   └─ All charts in spreadsheet

✅ sheets:///{spreadsheetId}/charts/{chartId}
   └─ Individual chart specification

✅ sheets:///{spreadsheetId}/pivots
   └─ Pivot table configurations

✅ sheets:///{spreadsheetId}/quality
   └─ Data quality analysis results

Representative resource families:

✅ Live sheet resources - spreadsheet metadata, ranges, charts, pivots, quality
✅ Schema resources - tool schemas and per-action guidance
✅ Guide resources - quota, batching, caching, and recovery guidance
✅ Decision trees - tool/strategy selection references
✅ Pattern and example libraries - workflow references and code examples
✅ Monitor resources - history, cache, metrics, discovery, transaction state
✅ Knowledge resources - formulas, templates, API notes, best practices

Prompts (40 workflows ✅)

Implemented & Tested:

✅ First-time setup - readiness, connection, first operation, full setup
✅ Analysis flows - auto analysis, comparison, history-aware analysis, performance audit
✅ Data quality flows - cleaning, automated remediation, quality masterclass
✅ Import/export flows - CSV, Excel, migration, bulk import
✅ Automation flows - sheet generation, batch optimization, pipelines
✅ Collaboration, visualization, troubleshooting, connector, and advanced scenario workflows

Tasks (SEP-1686 ✅)

Implemented Features:

✅ Background execution - Long-running ops don't block
✅ Progress reporting - Real-time operation status
✅ Cancellation support - AbortController integration
✅ Task store - In-memory (default) or Redis-backed
✅ Result persistence - Completed tasks accessible after execution
✅ Error propagation - Task errors returned to client

Example: Long-running import

// Start background import task
const task = await sheets_composite({
  action: 'import_csv',
  spreadsheetId: '...',
  csvData: largeDataset,
  targetRange: 'Sheet1!A1',
});
// Result: { taskId: 'task-123', progress: 0, status: 'running' }

// Poll for progress
const status = await getTaskStatus(task.taskId);
// { taskId: 'task-123', progress: 45, status: 'running' }

// Wait for completion
await waitForTask(task.taskId);
// { taskId: 'task-123', progress: 100, status: 'completed', result: {...} }

Elicitation (SEP-1036 ✅)

Implemented: User confirmations for destructive operations

✅ Confirmation requests - Ask before delete/overwrite
✅ Wizard patterns - Step-by-step guidance
✅ Effect scope validation - Warn about large operations
✅ Dry run preview - Preview changes before execution
✅ Undo/rollback - Automatic snapshots for recovery

Example: Safe deletion

// 1. Elicitation: Ask for confirmation
const confirm = await sheets_confirm({
  action: 'request',
  title: 'Delete 500 rows?',
  description: 'This operation will delete rows 2-501 from Sheet1',
  warning: 'This cannot be undone without using Sheets version history',
  suggestedAction: 'Create a version snapshot before proceeding',
});

// 2. User confirms or cancels

// 3. Create snapshot before destructive op
await sheets_collaborate({
  action: 'version_create_snapshot',
  description: 'Before bulk delete',
});

// 4. Execute deletion
await sheets_dimensions({
  action: 'delete_rows',
  sheetId: 0,
  startIndex: 1,
  endIndex: 501,
});

Sampling (SEP-1577 ✅)

Implemented: AI-powered analysis and recommendations

✅ Comprehensive analysis - Data quality, patterns, anomalies
✅ Pattern detection - Trends, correlations, seasonality
✅ Chart recommendations - Optimal visualizations
✅ Formula generation - Natural language → formulas
✅ Template suggestions - AI-generated spreadsheet templates
✅ Conflict detection - Concurrent modification warnings

Example: Generate formula from natural language

const analysis = await sheets_analyze({
  action: 'analyze_data',
  spreadsheetId: '...',
  range: 'Sales!A1:D100',
  question: 'Calculate total revenue for Q4',
});

// Result includes:
// - Pattern analysis (seasonality, trends)
// - Anomalies detected
// - Recommended formula: =SUM(D2:D100)
// - Chart recommendations: [line_chart, bar_chart, metric_chart]

Logging (Dynamic level control ✅)

Implemented: Runtime log level adjustment

# Initial log level (default: info)
export LOG_LEVEL=info

# Change at runtime (via logging/setLevel handler)
# Useful for debugging production issues without restart
curl -X POST http://localhost:9090/logging/setLevel \
  -d '{"level":"debug"}'

Log Levels: debug, info, warn, error

Transports (3 types ✅)

Implemented & Tested:

✅ STDIO - For Claude Desktop, local CLI
✅ Streamable HTTP - For hosted deployments, remote access, resumability
✅ Legacy SSE compatibility - For older clients that still require it

Configuration:

# STDIO (default)
npx servalsheets

# Streamable HTTP
PORT=3000 npm run start:http

# HTTP with OAuth
PORT=3000 npm run start:remote

Compliance Test Results

As of March 11, 2026, npm run test:all completed successfully with:

315 passed test files
53 skipped test files
8,613 passed tests
671 skipped tests

Protocol Compatibility

SDK Version: @modelcontextprotocol/sdk v1.29.0 MCP Version: 2025-11-25 TypeScript: Strict mode, 0 errors Node.js: 20+ required

Security Compliance

✅ OAuth 2.1 with PKCE
✅ Scoped permissions (per-user, per-resource)
✅ Token encryption (optional)
✅ Rate limiting (per-user)
✅ Input validation (Zod schemas)
✅ Error redaction (no token leakage)
✅ CORS configuration (production ready)
✅ HTTPS enforcement (production mode)

Production Readiness

✅ Error handling - Comprehensive with recovery strategies
✅ Monitoring - Metrics, logging, health checks
✅ Observability - OpenTelemetry tracing
✅ High availability - Redis session store, multi-instance
✅ Performance - Batching, caching, rate limiting
✅ Security - OAuth 2.1, encryption, token rotation
✅ Testing - 8,500+ tests, coverage reported in CI
✅ Documentation - 115+ pages, examples for all tools

License

MIT

Policies

Privacy Policy - Data handling and user rights
Security Policy - Vulnerability disclosure and best practices

Links

from github.com/khill1269/servalsheets

Установка ServalSheets

У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.

▸ github.com/khill1269/servalsheets

FAQ

ServalSheets MCP бесплатный?

Да, ServalSheets MCP бесплатный — установка в пару кликов через Unyly без оплаты.

Нужен ли API-ключ для ServalSheets?

Нет, ServalSheets работает без API-ключей и переменных окружения.

ServalSheets — hosted или self-hosted?

Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.

Как установить ServalSheets в Claude Desktop, Claude Code или Cursor?

Открой ServalSheets на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.

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Command Palette

ServalSheets

Описание

README

Connect to Claude

Option A — Claude.ai connector (no install)

Option B — Claude Desktop (local, STDIO)

Option C — Self-hosted

What's New (Post-v2.0.0, 2026-02-17 → Present)

v2.0.0 (2026-02-17)

Quick Start

Previous Releases

Features

Core Capabilities

MCP Protocol Support

Transport Support

Advanced Analytics 🔬

AI-Powered Features 🤖

Performance & Optimization ⚡

Developer Experience 🛠️

API Documentation 📚

Getting Started

Installation

Using with Claude Desktop (STDIO)

Using as Hosted Server (Streamable HTTP)

Hosted Failover (Optional)

OAuth Scope Modes

Documentation

🚀 Getting Started

📖 User Guides

🏭 Production & Operations

📚 Complete Documentation Index

🔐 Production

Contributing

Quick Links

Quick Start for Contributors

Core Principles

Verification Commands

Before Creating a PR

Tools Reference

Tool Summary (25 tools, 410 actions)

Examples

Example 1: Read and Analyze Data

Example 2: Safe Bulk Update with Dry Run

Example 3: Semantic Range Queries

Example 4: Create Charts

Example 5: Conditional Formatting

Marketplace Examples

Example 1: Sales Dashboard with XLOOKUP Formulas

Example 2: Quarterly Data Analysis with Charts

Example 3: Import CSV, Deduplicate, and Format as Table

Example 4: Automated Sharing and Version Snapshots

Example 5: Create Reusable Named Ranges And Protections

Safety Rails

Dry Run Mode

Effect Scope Limits

Expected State (Optimistic Locking)

Auto-Snapshot

Range Input Formats

Error Codes

Authentication

Service Account (Recommended for automation)

OAuth2 Access Token

OAuth2 Client Credentials

Encrypted Token Store (Optional)

Enterprise SSO (SAML 2.0)

Transport Security Model (RBAC)

Configuration

Required for Production (HTTP/Remote Server)

Optional: Redis for Session Storage

Rate Limiting

Caching

Observability

Effect Scope Limits

Logging

Timeouts

HTTP/2 and Connection Pool

Metrics Server

Memory Leak Detection

Example: Production Configuration