MCP Tool Lister

A Python application that connects to MCP (Model Context Protocol) servers and lists all available tools. Users can paste their MCP configuration and instantly see all tools from configured servers.

Features

• 🔌 Connect to multiple MCP servers simultaneously
• 📋 List all tools with detailed information (name, description, parameters)
• 🎨 Beautiful CLI interface with Rich formatting
• ✅ Configuration validation
• 🔍 Detailed parameter information for each tool

Prerequisites

• Python 3.8 or higher
• Node.js and npm (for running MCP servers like playwright, fetch, etc.)

Installation

1. Clone or download this project

2. Create a virtual environment (recommended):

python -m venv venv
.\venv\Scripts\Activate.ps1

3. Install dependencies:

pip install -r requirements.txt

Project Structure

MCP python/
├── app.py                 # Flask web application
├── app_mcp_browser.py    # MCP browser application
├── main.py               # CLI interactive tool
├── config.py             # Configuration parser
├── mcp_client.py         # MCP client wrapper
├── mcp_servers_config.py # Server configurations
├── requirements.txt      # Python dependencies
├── static/               # Web UI assets
├── templates/            # HTML templates
├── tests/                # Test suite (see tests/README.md)
└── README.md            # This file

Usage

Web Application

Run the Flask web application:

python app.py

Then open http://localhost:5000 in your browser.

Quick Test

Run the simple test with the included example config:

cd tests
python test_simple.py

Interactive Mode

1. Run the application:

python main.py

2. When prompted, paste your MCP configuration in JSON format. For example:

{
  "mcpServers": {
    "playwright": {
      "command": "npx",
      "args": [
        "@playwright/mcp@latest"
      ]
    },
    "fetch": {
      "command": "npx",
      "args": [
        "@modelcontextprotocol/server-fetch"
      ]
    }
  }
}

3. Press Enter twice after pasting your configuration

4. The application will:

   • Parse your configuration
   • Connect to each MCP server
   • List all available tools with details

Configuration Format

Your MCP configuration should follow this format:

{
  "mcpServers": {
    "server-name": {
      "command": "command-to-run",
      "args": ["arg1", "arg2"],
      "env": {
        "ENV_VAR": "value"
      }
    }
  }
}

Fields:

• command (required): The command to execute the MCP server
• args (optional): Array of command-line arguments
• env (optional): Environment variables to pass to the server

Examples

Playwright MCP Server

{
  "mcpServers": {
    "playwright": {
      "command": "npx",
      "args": ["@playwright/mcp@latest"]
    }
  }
}

Multiple Servers

{
  "mcpServers": {
    "playwright": {
      "command": "npx",
      "args": ["@playwright/mcp@latest"]
    },
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/allowed/directory"]
    },
    "fetch": {
      "command": "npx",
      "args": ["@modelcontextprotocol/server-fetch"]
    }
  }
}

Output

The application displays:

• Connection status for each server
• Number of tools found per server
• Detailed table for each server showing:
  • Tool name
  • Description
  • Parameters (with types and required/optional status)

Resources

• MCP Use Library
• MCP Market - Find more MCP servers
• Playwright MCP Server

Additional Features

Web Search + OpenAI Integration

See WEB_SEARCH_README.md for details on using web search MCP servers with OpenAI API.

MCP Browser

See MCP_BROWSER_README.md for details on the browser-based interface.

MCP Server Guide

See MCP_SERVER_GUIDE.md for comprehensive information about MCP servers.

Testing

All test files are located in the tests/ directory. See tests/README.md for details on running tests.

Deployment

Deploy to Render

This application is ready for easy deployment to Render.

Quick Deploy (Using Blueprint)

1. Push your code to GitHub
2. Go to Render Dashboard
3. Click "New" → "Blueprint"
4. Connect your GitHub repository
5. Render will automatically detect render.yaml and configure your service
6. Click "Apply" to deploy

Manual Deploy

1. Push your code to GitHub
2. Go to Render Dashboard
3. Click "New" → "Web Service"
4. Connect your GitHub repository
5. Configure:
   • Name: mcp-tool-lister (or your choice)
   • Runtime: Python 3
   • Build Command: pip install -r requirements.txt
   • Start Command: gunicorn app:app --bind 0.0.0.0:$PORT --workers 2 --timeout 120
   • Plan: Free
6. Add Environment Variables (if needed):
   • OPENAI_API_KEY: Your OpenAI API key (optional, for AI features)
7. Click "Create Web Service"

Environment Variables

Optional environment variables you can set on Render:

• OPENAI_API_KEY: For OpenAI integration features
• OPENAI_MODEL: OpenAI model to use (default: gpt-4-turbo-preview)

After Deployment

Your application will be available at: https://your-service-name.onrender.com

Note: The free tier on Render may spin down after inactivity. The first request after inactivity may take 30-60 seconds to respond.

Troubleshooting

Connection Issues

• Ensure Node.js and npm are installed and in your PATH
• Some MCP servers require additional setup or API keys
• Check that the command and args in your configuration are correct

Module Not Found

• Make sure you've installed all requirements: pip install -r requirements.txt
• Activate your virtual environment if using one

License

MIT License - Feel free to use and modify this project