Mcplab Mcp Server

Test, debug and evaluate Model Context Protocol servers with LLMs

Test how well LLM agents use your MCP tools, compare different models, and track quality over time with automated testing and detailed reports.

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What is MCPLab?

MCPLab is a testing and evaluation framework for MCP servers. It helps you:

• Validate that LLM agents correctly use your MCP tools
• Compare different LLMs (Claude, GPT-4, etc.) on the same tasks
• Track tool usage patterns, success rates, and performance metrics
• Automate quality assurance in CI/CD pipelines
• Debug agent behavior with detailed execution traces

Perfect for MCP server developers who want to ensure their tools work reliably across different AI models.

MCPLab

Visit mcplab.inspectr.dev to learn more.

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✨ Features

Core Capabilities

• HTTP SSE Transport - Test MCP servers over Streamable HTTP
• Multi-LLM Support - OpenAI, Anthropic Claude, Azure OpenAI
• Rich Assertions - Validate tool usage, sequences, and response content
• Judge Agent Checks - Semantic validation using an LLM judge for fuzzy, natural-language assertions
• Variance Testing - Run multiple iterations to measure stability
• Detailed Traces - JSONL logs of every tool call and LLM response

Analysis & Reporting

• Trend Analysis - Track pass rates and performance over time
• LLM Comparison - Built-in tools to compare agent behavior
• Multiple Outputs - HTML report, JSON results, Markdown summary, JSONL trace
• Custom Metrics - Extract values and track domain-specific KPIs
• Markdown Reports - Store and browse custom analysis notes alongside runs

AI-Powered Tools (App Mode)

• Scenario Assistant - AI chat to help design and refine eval scenarios
• Result Assistant - AI chat to analyze and explain completed run results
• MCP Tool Analysis - Automated review of MCP tool quality and safety
• Judge Agent Checks - Configure a judge LLM alongside your deterministic checks

Developer Experience

• Watch Mode - Auto-rerun tests when configs change
• YAML Configuration - Declarative, version-controllable eval specs
• Interactive Reports - Self-contained HTML with filtering and drill-down
• Multi-Agent Testing - Compare LLMs with a single CLI flag
• Scenario Isolation - Run specific tests or full suites

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🚀 Quick Start

1. Install

npx @inspectr/mcplab --help

Or install globally:

npm install -g @inspectr/mcplab

2. Set up environment

cp .env.example .env
# Edit .env and add your API keys:
# ANTHROPIC_API_KEY=sk-ant-...
# OPENAI_API_KEY=sk-...

Add your API keys to .env. See Environment Variables for full examples.

3. Run your first evaluation

# Run the app (frontend + local API bridge)
npx @inspectr/mcplab app --open

# Run an evaluation from a config file
npx @inspectr/mcplab run -c mcplab/evals/eval.yaml

# View the results
open mcplab/results/evaluation-runs/$(ls -t mcplab/results/evaluation-runs | head -1)/report.html

4. Create your own test

Create my-eval.yaml:

servers:
  - id: my-server
    transport: "http"
    url: "http://localhost:3000/mcp"

agents:
  - id: claude
    provider: "anthropic"
    model: "claude-haiku-4-5-20251001"
    temperature: 0
    max_tokens: 2048

scenarios:
  - id: "basic-test"
    servers: ["my-server"]
    prompt: "Use the available tools to complete this task..."
    eval:
      tool_constraints:
        required_tools: ["my_tool"]
      response_assertions:
        - type: "regex"
          pattern: "success|completed"

Run it:

mcplab run -c my-eval.yaml

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📖 Configuration Guide

Structure Overview

Add this at the top of your eval file for editor validation/autocomplete:

# yaml-language-server: $schema=./config-schema.json

servers:     # MCP servers to test against
  - id: local-server
    transport: "http"
    url: "http://localhost:3000/mcp"
  - ref: "shared-server"

agents:      # LLM agents to use for testing
  - id: local-agent
    provider: "anthropic"
    model: "claude-sonnet-4-6"
  - ref: "claude-sonnet-46"

scenarios:   # Test scenarios to run
  - id: "basic-test"
    servers: ["local-server"]
    prompt: "..."
  - ref: "scn-shared-basic"

Servers

Define MCP servers with connection details and authentication:

servers:
  - id: my-server
    transport: "http"
    url: "https://api.example.com/mcp"
    auth:
      type: "bearer"
      token: ${MCP_TOKEN}        # env var reference
      # token: my-secret-token   # or direct value

Authentication types:

Bearer Token:

auth:
  type: "bearer"
  token: ${MCP_TOKEN}        # env var reference
  # token: my-secret-token   # or direct value

API Key:

auth:
  type: "api_key"
  header_name: "X-API-Key"     # optional, defaults to X-API-Key
  value: ${MY_API_KEY}

OAuth Client Credentials:

auth:
  type: "oauth_client_credentials"
  token_url: "https://auth.example.com/token"
  client_id_env: "CLIENT_ID"
  client_secret_env: "CLIENT_SECRET"
  scope: "read:data"              # Optional
  audience: "https://api.example.com"  # Optional

Agents

Configure LLM agents with provider-specific settings:

Anthropic (Claude):

agents:
  - id: claude-sonnet
    provider: "anthropic"
    model: "claude-sonnet-4-6"
    temperature: 0
    max_tokens: 2048
    system: "You are a helpful assistant."

OpenAI (ChatGPT):

agents:
  - id: gpt-4
    provider: "openai"
    model: "gpt-4o-mini"
    temperature: 0
    max_tokens: 2048
    system: "You are a helpful assistant."

Azure OpenAI:

agents:
  - id: azure-gpt
    provider: "azure_openai"
    model: "gpt-4o"  # Deployment name
    temperature: 0
    max_tokens: 2048
    system: "You are a helpful assistant."

Required environment variables:

• Anthropic: ANTHROPIC_API_KEY
• OpenAI: OPENAI_API_KEY
• Azure: AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_API_KEY, AZURE_OPENAI_DEPLOYMENT

Scenarios

Define test scenarios with prompts and evaluation criteria:

scenarios:
  - id: "search-and-analyze"
    servers: ["my-server"]
    prompt: |
      Search for items matching criteria X,
      then analyze the results and provide insights.

    eval:
      # Validate tool usage
      tool_constraints:
        required_tools: ["search", "analyze"]
        forbidden_tools: ["delete"]

      # Validate tool order
      tool_sequence: ["search", "analyze"]

      # Validate response content
      response_assertions:
        - type: "contains"
          value: "found"
        - type: "not_contains"
          value: "error"
        - type: "regex"
          pattern: "found \\d+ items"
        - type: "jsonpath"
          path: "$.summary.count"
          equals: 10
        - type: "jsonpath_exists"
          path: "$.summary.items"

    # Semantic checks via LLM judge
    agent_assertions:
      - label: "response mentions a time range"
        prompt: "Does the response include a valid time range (e.g. start and end date)?"
      - label: "no error messages in response"
        prompt: "Is the response free of error messages or failure indicators?"

    # Optional: give the judge extra context
    agent_context:
      include_prompt: true           # send the scenario prompt to the judge
      include_tool_sequence: true    # send the list of tools called

    # Extract metrics
    extract:
      - name: "item_count"
        from: "final_text"
        regex: "found (?<value>\\d+) items"

run_defaults:
  selected_agents:
    - claude-sonnet

Evaluation options:

• tool_constraints - Which tools must/must not be used

  • required_tools: Tools that must be called
  • forbidden_tools: Tools that must not be called

• tool_sequence - Ordered tool list that must appear in the run in the same order

  • Example: ["search", "analyze"] passes when the run contains search before analyze, even if other tools happen in between

• response_assertions - Validate the final response

  • contains: Final text must include a literal substring (case-insensitive)
  • not_contains: Final text must not include a literal substring (case-insensitive)
  • starts_with: Final text must start with a literal substring (case-insensitive)
  • ends_with: Final text must end with a literal substring (case-insensitive)
  • equals: Final text must exactly equal a literal string (case-insensitive)
  • regex: Pattern matching on response text
  • jsonpath: Query and validate JSON responses (with optional equals)
  • jsonpath_exists: JSONPath must resolve at least one value
  • jsonpath_not_exists: JSONPath must resolve no values

• agent_assertions - Semantic checks evaluated by an LLM judge

  • Each assertion has a label and a freeform prompt describing what to verify
  • All checks in a scenario are batched into a single judge request
  • Requires a default evaluation judge configured in workspace settings

• agent_context - Optional extra context sent to the judge alongside each check

  • include_prompt: sends the original scenario prompt to the judge
  • include_tool_sequence: sends the list of tool names called during the run

• extract - Extract metrics from responses

  • Capture values using regex named groups: (?<value>...)

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🔑 Environment Variables

Add your LLM Agent API keys to .env for each provider you want to use:

Anthropic (Claude models):

# -----------------------------------------------------------------------------
# Anthropic Configuration
# -----------------------------------------------------------------------------
# Required for testing Claude models (claude-haiku-4, claude-sonnet-4)

ANTHROPIC_API_KEY=sk-ant-api03-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

Azure OpenAI (GPT models):

# -----------------------------------------------------------------------------
# Azure OpenAI Configuration
# -----------------------------------------------------------------------------
# Required for testing GPT models (gpt-4o-mini, gpt-4o, etc.)

AZURE_OPENAI_API_KEY=XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com
AZURE_OPENAI_DEPLOYMENT="gpt-5.2-chat"
AZURE_OPENAI_API_VERSION="2025-04-01-preview"

OpenAI:

# -----------------------------------------------------------------------------
# OpenAI Configuration
# -----------------------------------------------------------------------------
OPENAI_API_KEY=sk-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

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💡 Usage Examples

Basic Usage

# Run all scenarios
mcplab run -c mcplab/evals/eval.yaml

# Interactive mode (choose config + agents)
mcplab run --interactive

# Run specific scenario
mcplab run -c mcplab/evals/eval.yaml -s basic-test

# Run with variance testing (5 iterations)
mcplab run -c mcplab/evals/eval.yaml -n 5

# Runtime MCP server override for all selected scenarios
mcplab run -c mcplab/evals/eval.yaml --server-override-all kpi-api-stage

# Runtime per-scenario override (wins over --server-override-all)
mcplab run -c mcplab/evals/eval.yaml \
  --server-override-all kpi-api-stage \
  --server-override add-calculations=kpi-api-dev

Runtime override server refs are resolved against the effective server set (eval config + mcplab/servers.yaml) consistently across CLI, app-server API, and MCP tools.

App Mode

Serve the web app and local API in one process:

mcplab app --open

# Interactive startup (host/port/paths prompt + summary)
mcplab app --interactive

Optional custom paths:

mcplab app --evals-dir mcplab/evals --runs-dir mcplab/results/evaluation-runs --port 8787 --open

Optional development mode (proxy frontend to Vite, keep API local):

mcplab app --dev

Multi-LLM Testing

Compare how different LLMs perform on the same tasks:

# Test with multiple agents
mcplab run -c examples/eval.yaml \
  --agents claude-haiku,gpt-4o-mini,gpt-4o

# This runs each scenario with each agent automatically
# 3 scenarios × 3 agents = 9 tests

# Compare results
node scripts/compare-llm-results.mjs mcplab/results/evaluation-runs/LATEST/results.json

Output:

📊 LLM Performance Comparison

LLM              | Pass Rate | Avg Tools/Run | Avg Duration (ms)
-----------------|-----------|---------------|------------------
claude-haiku     |    100.0% |           2.5 |               850
gpt-4o-mini      |     88.9% |           2.8 |               950
gpt-4o           |     88.9% |           3.2 |              1200

💡 Key Insights
• Highest Pass Rate: claude-haiku (100.0%)
• Fastest: claude-haiku (850ms avg)
• Most Efficient: claude-haiku (2.5 tools/run)

Watch Mode

Auto-rerun tests when config changes:

mcplab watch -c examples/eval.yaml

# With multi-agent testing
mcplab watch -c examples/eval.yaml \
  --agents claude-haiku,gpt-4o-mini

Generate Reports

# Regenerate HTML report from previous run
mcplab report --input mcplab/results/evaluation-runs/20260206-212239

# Interactive run selection from recent runs
mcplab report --interactive

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🤖 AI-Powered Features

These features are available through the web app (mcplab app).

Scenario Assistant

An interactive AI chat that helps you design and refine evaluation scenarios. Given a scenario, it can suggest improvements to the prompt, evaluation rules, and extraction patterns — and can call your MCP server's tools directly to demonstrate expected behavior.

Open the app, navigate to an eval, and open the Scenario Assistant panel on any scenario.

Result Assistant

An AI chat that analyzes completed evaluation runs. Ask it to explain failures, identify patterns across scenarios, or summarize what went wrong in a specific run. It has read-only access to run artifacts, traces, and results.

Open a run in the app and click Result Assistant.

MCP Tool Analysis

Automated quality review of your MCP server's tools. Connects to your server, discovers all tools, and produces a report covering:

• Name and description quality
• Schema completeness
• Safety classification (read-like vs. potentially destructive)
• Sample call behavior (optional — runs real calls against your server)

Reports are saved to mcplab/results/tool-analysis/ and viewable in the app.

Navigate to Tool Analysis in the app sidebar to start an analysis job.

Markdown Reports

Store and browse custom analysis notes, comparison docs, or generated reports alongside your eval runs. Place .md files in mcplab/reports/ and they become accessible in the app under Reports.

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📚 Reusable configurations

Define servers, agents, and scenarios once and reuse them across multiple eval files.

mcplab/
├── servers.yaml       # Shared MCP server definitions
├── agents.yaml        # Shared LLM agent definitions
└── scenarios/
    ├── scenario-a.yaml
    └── scenario-b.yaml

Reference library items in eval configs:

servers:
  - ref: "my-server"          # from servers.yaml
agents:
  - ref: "claude-sonnet"      # from agents.yaml
scenarios:
  - ref: "scenario-a"         # from scenarios/scenario-a.yaml

Libraries can be managed through the app's Libraries page.

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📂 Output Structure

Each evaluation run creates a timestamped directory:

mcplab/results/evaluation-runs/20260206-212239/
├── trace.jsonl        # Detailed execution log (every tool call, LLM response)
├── results.json       # Structured results (pass/fail, metrics, aggregates)
├── summary.md         # Human-readable summary table
└── report.html        # Interactive HTML report (self-contained)

Other output directories:

mcplab/
├── evals/                          # Eval definition YAML files
├── results/
│   ├── evaluation-runs/            # Run artifacts
│   └── tool-analysis/              # Saved tool analysis reports
├── reports/                        # Custom markdown reports
├── servers.yaml                    # Library: shared server definitions
├── agents.yaml                     # Library: shared agent definitions
└── scenarios/                      # Library: shared scenario files

Trace Format (JSONL)

{"type":"run_started","run_id":"...","ts":"2026-02-06T20:03:54.585Z"}
{"type":"scenario_started","scenario_id":"search-tags","agent":"claude-haiku","ts":"..."}
{"type":"llm_request","messages_summary":"user:Search for tags...","ts":"..."}
{"type":"llm_response","raw_or_summary":"tool_calls:search_tags","ts":"..."}
{"type":"tool_call","server":"demo","tool":"search_tags","args":{...},"ts_start":"..."}
{"type":"tool_result","server":"demo","tool":"search_tags","ok":true,"result_summary":"...","ts_end":"...","duration_ms":1114}
{"type":"final_answer","text":"Found 42 tags matching...","ts":"..."}
{"type":"scenario_finished","scenario_id":"search-tags","pass":true,"metrics":{...},"ts":"..."}

Results Format (JSON)

{
  "metadata": {
    "run_id": "20260206-212239",
    "timestamp": "2026-02-06T20:22:39.000Z",
    "config_hash": "abc123...",
    "git_commit": "def456..."
  },
  "summary": {
    "total_scenarios": 8,
    "total_runs": 8,
    "pass_rate": 1.0,
    "avg_tool_calls_per_run": 2.5,
    "avg_tool_latency_ms": 950
  },
  "scenarios": [...]
}

---

🎓 Real-World Examples

Example 1: Weather MCP Server

Test a weather data MCP server:

# Run comprehensive test suite (9 scenarios)
mcplab run -c examples/eval-weather-comprehensive.yaml

# Test a specific scenario
mcplab run -c examples/eval-weather-comprehensive.yaml \
  -s forecast-accuracy

# Compare Claude vs GPT-4 on all scenarios
mcplab run -c examples/eval-weather-simple.yaml \
  --agents claude-haiku,gpt-4o-mini

Included scenarios:

• Current conditions lookup
• Multi-day forecast retrieval
• Location search and resolution
• Severe weather alerts
• Historical data queries
• Unit conversion (metric/imperial)

Example 2: Multi-Agent Comparison

Create multi-agent-eval.yaml with one agent defined:

agents:
  - id: claude-haiku
    provider: anthropic
    model: claude-haiku-4-5-20251001
  - id: gpt-4o-mini
    provider: openai
    model: gpt-4o-mini
  - id: gpt-4o
    provider: openai
    model: gpt-4o

scenarios:
  - id: "complex-task"
    prompt: "..."

run_defaults:
  selected_agents:
    - claude-haiku

Run with all agents:

mcplab run -c multi-agent-eval.yaml \
  --agents claude-haiku,gpt-4o-mini,gpt-4o \
  -n 5

# 1 scenario × 3 agents × 5 runs = 15 tests

Example 3: CI/CD Integration

Add to .github/workflows/mcp-eval.yml:

name: MCP Evaluation

on: [push, pull_request]

jobs:
  evaluate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: '22'

      - run: npm install
      - run: npm run build

      - name: Run evaluations
        run: mcplab run -c examples/eval.yaml -n 3
        env:
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}

      - name: Upload results
        uses: actions/upload-artifact@v4
        with:
          name: evaluation-results
          path: mcplab/results/evaluation-runs/

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🛠️ Advanced Features

Custom Analysis Scripts

Analyze results with custom logic:

// my-analysis.mjs
import { readFileSync } from 'fs';

const results = JSON.parse(readFileSync('mcplab/results/evaluation-runs/LATEST/results.json'));

// Calculate custom metrics
for (const scenario of results.scenarios) {
  const efficiency = scenario.pass_rate / scenario.runs[0].tool_call_count;
  console.log(`${scenario.scenario_id}: ${efficiency.toFixed(2)} success/tool`);
}

Generate Multi-LLM Configs

Auto-generate multi-agent configs:

# Creates eval-weather-multi-llm.yaml
node scripts/generate-multi-llm-config.mjs examples/eval-weather.yaml

Compare LLM Performance

Built-in comparison script:

node scripts/compare-llm-results.mjs mcplab/results/evaluation-runs/20260206-212239/results.json

Shows:

• Pass rates by LLM
• Tool usage efficiency
• Response times
• Scenario-by-scenario breakdown

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🔧 Development

Project Structure

mcp-evaluation/
├── packages/
│   ├── cli/           # CLI tool (run, watch, report, app commands)
│   ├── app/           # Web frontend (React)
│   ├── core/          # Evaluation engine, agent adapters, MCP client
│   └── reporting/     # HTML report generation
├── examples/          # Example evaluation configs
├── scripts/           # Utility scripts (multi-LLM, comparison)
├── mcplab/results/    # Evaluation results + analysis (gitignored)
└── .claude/           # Claude Code skills (optional)

Run in Development Mode

# Build all packages
npm run build

# Run CLI directly with tsx (no build needed)
npm run dev -- app --dev

# Or run just the frontend dev server
npm run app:dev:ui

Run Tests

npm test

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🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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License

MIT License - see LICENSE for details.

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Acknowledgments

Built with:

• Model Context Protocol SDK
• Anthropic SDK
• OpenAI SDK

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Support

• Issue Tracker
• Discussions
• MCP Protocol

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