Lintbase

Ground Truth for AI Coding Agents. LintBase gives AI agents real-time knowledge of your database schema, security rules, and architecture so they stop hallucinating your codebase.

npx lintbase export-context firestore --key ./service-account.json

npm version npm downloads License: MIT

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Why LintBase?

Developers are constantly feeding context files to AI tools like Cursor, Windsurf, Copilot Workspace, and Claude Code. If your agent doesn't understand your real database schema, it writes code that fails in production.

LintBase acts as the bridge. It connects directly to your database, reads the ground truth of your live documents, and generates structured context optimized for AI.

• 🤖 Stops AI Hallucinations — Generates exact schema, field presence rates, and types.
• 📐 Catches Schema Drift — CI protection with lintbase check against schema snapshots.
• 🔒 Security Context — Highlights missing rules or exposed PII before your AI writes queries.
• 💸 Cost Awareness — Prevents AI from writing unbounded queries on 2M+ document collections.
• 🍃 Universal NoSQL — Works effortlessly with Firestore and MongoDB.

Why not just let the agent read the code?

Because in document databases, the code lies. The real schema is whatever your live documents actually contain, and that drifts away from the code with every half-finished migration, every renamed field, and every previous AI session that wrote in a hurry. An agent inferring the schema from TypeScript interfaces writes plausible queries against a database that no longer exists. The failure is silent: empty results, undefined values, a new field variant living alongside the old one. LintBase reads the documents, not the code.

Limitations (honest ones)

• Firestore and MongoDB only. Postgres is the obvious gap and it is next.
• Large collections are sampled, not fully scanned. Presence rates are estimates on multi-million-document collections.
• The MCP server is newer than the CLI. Expect rough edges there first.

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🤖 AI Context Export (For Cursor, Claude, Windsurf)

The fastest way to give your AI agent perfect database knowledge.

npx lintbase export-context firestore --key ./service-account.json

Output:

/lintbase-context/
├── database-schema.md
├── collections.md
├── security-rules.md
├── architecture.md
└── risk-report.md

Drop the lintbase-context folder into your AI's context window, or mention it in .cursorrules. Your agent will now write perfect, drift-free database queries.

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Quick Start

1. Get a service account key

Firebase Console → Project Settings → Service Accounts → Generate new private key

Save the JSON file. Never commit it to git.

2. CI Pipeline Protection (Schema Drift)

LintBase acts as "Version Control for your Schema". Run the snapshot command to create a baseline:

npx lintbase snapshot firestore --key ./service-account.json

Commit .lintbase/schema.json to your repository. Then, add the check command to your CI/CD pipeline (GitHub Actions, GitLab CI):

npx lintbase check firestore --key ./service-account.json --fail-on error

If a query or deployment accidentally deletes a critical field or changes a type (e.g., string to number), your CI build will fail instantly.

3. Run a general scan

npx lintbase scan firestore --key ./service-account.json

You'll see a full report in your terminal:

 LintBase — Firestore Scan
 ─────────────────────────────────────────────
 Collections scanned:  12
 Documents sampled:    847
 Issues found:         23  (4 errors · 11 warnings · 8 infos)
 Risk score:           67 / 100  [HIGH]

 ERRORS
 ✖  users         no-auth-check        Documents readable without authentication
 ✖  orders        missing-index        Query on `status` + `createdAt` has no composite index
 ✖  debug_logs    large-collection     Collection has 2.4M docs — estimated $340/mo in reads

 WARNINGS
 ⚠  products      schema-drift         Field `price` found as both Number and String
 ⚠  sessions      ttl-missing          No expiry field — stale docs accumulate indefinitely
 ...

3. Save to your dashboard (optional)

Track your database health over time at lintbase.com:

npx lintbase scan firestore \
  --key ./service-account.json \
  --save https://www.lintbase.com \
  --token <your-api-token>

Get your token at lintbase.com/dashboard/settings.

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Supported Databases

• Firestore: npx lintbase scan firestore --key ./sa.json
• MongoDB: npx lintbase scan mongodb --uri mongodb+srv://user:[email protected]/test

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🤖 AI Agent Integration (MCP)

Using Cursor, Claude Desktop, or Windsurf? Install lintbase-mcp to give your AI agent real-time Firestore schema context — so it stops hallucinating field names.

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "lintbase": {
      "command": "npx",
      "args": ["-y", "lintbase-mcp"]
    }
  }
}

Now when you ask your AI "add a field to users", it will check your real schema first before writing a line of code.

→ Full setup guide & tools reference

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What it catches

🔒 Security

Rule	What it detects
no-auth-check	Collections readable/writable without auth
exposed-pii	Email, phone, SSN fields without encryption markers
world-readable	Documents with overly permissive security rules

💸 Cost

Rule	What it detects
large-collection	Collections with 100k+ docs and high read cost
unbounded-query	Queries without limit() that scan entire collections
missing-index	Filter combinations that fall back to full collection scans
debug-collection	Collections that look like temporary data that was never cleaned up

📐 Schema Drift

Rule	What it detects
type-inconsistency	Field stored as different types across documents
missing-required-field	Field present in 90%+ of docs but absent in some
nullable-id	Reference fields that are sometimes null

⚡ Performance

Rule	What it detects
deep-nesting	Document fields nested > 3 levels deep
large-document	Documents approaching the 1MB Firestore limit
hot-document	Single document updated by many users simultaneously
no-pagination	Collections without a standard pagination field

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Options

lintbase <command> <database> [options]

Commands:
  scan <database>             Scan a database and print diagnostic report
  export-context <database>   Export schema to markdown/JSON for AI agents
  snapshot <database>         Generate local schema snapshot for CI comparison
  check <database>            Run in headless CI mode (fails on schema drift)

Options:
  --key <path>      Path to Firebase service account JSON 
  --uri <uri>       MongoDB connection URI
  --limit <n>       Max documents to sample per collection     [default: 100]
  --fail-on <lvl>   Fail pipeline if issues exceed severity (error, warning, info)
  --save <url>      Dashboard URL to save results
  --token <token>   API token for dashboard (from lintbase.com)
  --collections     Comma-separated list of collections to scan
  -h, --help        Show help

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Dashboard

The CLI is free forever. The dashboard visualizes your scan results as an interactive schema map — your credentials never leave your machine.

What Pro gets you via --save:

• ⬡ Schema Map — every collection as a draggable card, with real field names, types, presence rates, and issue badges
• ◎ Health Radar — per-collection spider chart across Schema, Security, Performance, and Cost axes
• ⊕ Priority Quadrant — 2×2 bubble chart of Impact vs. Ease of Fix — tells you what to fix first
• ≋ Drift Timeline — stored history across scans so you can replay your schema architecture over time.

CLI Local Tooling: 100% Free · Pro: $39/month — unlimited history, dashboards, and shared team workflow.

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Security

• Your service account key never leaves your machine — it is only read locally
• Document sampling is hard-capped at --limit (default 100) to prevent accidental read costs
• The --save flag only sends the scan summary and issue list — never raw document data

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License

MIT © Mamadou Dia