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MCP verified Apache-2.0 Self-run

Mcp Lab

mcp-binabik-ai-mcp-lab · by binabik-ai

Package for testing MCP servers with different LLM providers

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Install

$ agentstack add mcp-binabik-ai-mcp-lab

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access Used
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets Used
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
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Declared compatibility

Claude CodeClaude DesktopCursorWindsurf

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.

How agent discovery & health will work →
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About

🧪 MCP Lab

A focused testing environment for MCP (Model Context Protocol) servers with multiple LLM providers. Compare how different models handle tool calling, track metrics, and analyze performance.

Features

  • Multi-Provider Support: Test with Groq, OpenAI, and Anthropic models
  • MCP Server Integration: Connect to multiple MCP servers simultaneously
  • Smart Media Rendering: Automatically embed plots, images, and iframes from tool outputs
  • Detailed Metrics: Track tokens, latency, tool calls, and iterations
  • Debug Mode: Toggle detailed tool execution visibility
  • Analytics Dashboard: Built-in database viewer with filtering and export
  • SQLite Storage: All conversations saved with full metadata for analysis

Quick Start

1. Install Dependencies

pip install -r requirements.txt

2. Configure Environment

cp .env.example .env
# Edit .env with your API keys

Required: At least one LLM provider API key:

3. Configure MCP Servers

Edit config/mcp_config.json:

{
  "mcpServers": {
    "rosbag_reader": {
      "command": "/path_to_venv/bin/python",
      "args": [
        "/path_to_ws/mcp-rosbags/src/server.py"
      ],
    }
  }
}

5. Run the Application

python run.py

Open your browser to:

  • Main Lab: http://localhost:8000

Project Structure

mcp-lab/
├── app.py                  # FastAPI backend
├── config.py              # Configuration
├── run.py                 # Application launcher
├── requirements.txt       # Python dependencies
├── .env                   # Your API keys (create from .env.example)
├── mcp_lab.db            # SQLite database (created automatically)
│
├── config/
│   └── mcp_config.json   # MCP server configurations
│
├── services/
│   ├── llm_service.py    # Multi-provider LLM management
│   ├── mcp_service.py    # MCP server management
│   ├── mcp_tools_bridge.py # Tool conversion bridge
│   └── db_service.py     # Database operations
│
├── handlers/
│   ├── chat_handler.py   # Chat message processing
│   └── websocket_manager.py # WebSocket connections
│
└── frontend/
    ├── index.html        # Main chat interface
    ├── analytics.html    # Analytics dashboard
    └── style.css        # Styling

Usage Guide

Testing MCP Tools

  1. Start a conversation - The AI will automatically discover available tools
  2. Toggle Debug Mode - See detailed tool execution, arguments, and results
  3. Compare Providers - Switch between providers/models to compare behavior

Understanding Metrics

  • Tokens: Approximate token count for the exchange
  • Latency: Total time from request to response
  • Tools Called: Number of MCP tool invocations
  • Iterations: Number of reasoning cycles (simple agent in a loop)

Media Rendering

The system automatically detects and renders:

  • file:///path/to/plot.html → Embedded iframe
  • Image files → Inline images
  • Plotly/chart URLs → Embedded visualizations

Analytics Dashboard

  • View all conversations with filtering
  • Compare provider/model performance
  • Export data as JSON or CSV
  • Track average metrics per provider

Adding MCP Servers

Add to config/mcp_config.json:

{
  "mcpServers": {
    "your-server": {
      "command": "path-to-executable",
      "args": ["arg1", "arg2"],
      "env": {
        "ENV_VAR": "value"
      }
    }
  }
}

Troubleshooting

MCP Servers Not Connecting

  • Verify MCP server packages are installed
  • Check the command paths in mcp_config.json
  • Look at console logs for specific error messages

No Models Available

  • Verify at least one API key is set in .env
  • Check that the API key is valid
  • Ensure you have access to the models

Database Issues

  • Delete mcp_lab.db to start fresh
  • Check write permissions in the directory

Debug Mode

Set DEBUG=True in .env for:

  • Auto-reload on code changes
  • Detailed logging
  • Error stack traces

License

Apache 2

Acknowledgments

Built for testing MCP (Model Context Protocol) servers with various LLM providers. MCP is originally developed by Anthropic.

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.