Tool Gating MCP

An intelligent proxy/router for Model Context Protocol (MCP) that enables Claude Desktop and other MCP clients to dynamically discover and use tools from multiple MCP servers while maintaining a single connection point. This system prevents context bloat by intelligently selecting only the most relevant tools for each task.

🎯 The Problem

MCP clients like Claude Desktop must load all servers at startup and cannot dynamically add servers during conversations. When using multiple MCP servers:

• Exa server: 7 search tools (web, research papers, Twitter, companies, etc.)
• Puppeteer: Browser automation tools
• Context7: Documentation search tools
• Desktop Commander: 18+ desktop automation tools

Loading all servers directly leads to:

• 🚨 Context bloat: 100+ tools consuming most of the context window
• 🔒 Static configuration: Cannot add servers without restarting Claude
• 💸 Increased costs: More tokens consumed per request
• 🎯 Poor tool selection: AI struggles to choose from too many options

💡 The Solution

Tool Gating MCP acts as an intelligent proxy that:

1. Single Connection: Claude Desktop connects only to Tool Gating
2. Backend Management: Maintains connections to multiple MCP servers
3. Smart Discovery: Finds relevant tools across all servers using semantic search
4. Dynamic Provisioning: Loads only needed tools within token budgets
5. Transparent Routing: Executes tools on appropriate backend servers

Example: Instead of configuring 10 MCP servers with 100+ tools, configure just Tool Gating. Then dynamically discover and use only the 2-3 tools you need.

🚀 Features

• Proxy Architecture: Single MCP server that routes to multiple backend servers
• Dynamic Tool Discovery: Find tools across all servers without manual configuration
• Semantic Search: Natural language queries to find the right tools
• Smart Provisioning: Load only relevant tools within token budgets
• Transparent Execution: Route tool calls to appropriate backend servers
• Native MCP Server: Direct integration with Claude Desktop via mcp-proxy
• Cross-Server Intelligence: Unified view of tools from Puppeteer, Exa, Context7, etc.
• Token Optimization: 90%+ reduction in context usage vs. loading all servers
• Zero Configuration: Claude Desktop needs only Tool Gating configuration

📋 Prerequisites

• Python 3.12+
• uv package manager

🔧 Installation

1. Clone the repository:

git clone https://github.com/yourusername/tool-gating-mcp.git
cd tool-gating-mcp

2. Create and activate virtual environment:

uv venv
source .venv/bin/activate  # On Unix/macOS
# .venv\Scripts\activate  # On Windows

3. Install dependencies:

uv sync

4. Install package in development mode:

uv pip install -e .

🏃 Running the Server

As HTTP API Server

# Start the server
tool-gating-mcp

# Or with uvicorn for development
uvicorn tool_gating_mcp.main:app --reload

The server will run on http://localhost:8000

API documentation available at:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc
• MCP Endpoint: http://localhost:8000/mcp (SSE transport)

As MCP Server (Recommended)

Tool Gating MCP is now a native MCP server that works directly with Claude Desktop:

1. Start the server:

   tool-gating-mcp
   

2. Install mcp-proxy (if not already installed):

   uv tool install mcp-proxy
   

3. Add to Claude Desktop:

   Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

   {
     "mcpServers": {
       "tool-gating": {
         "command": "/Users/YOUR_USERNAME/.local/bin/mcp-proxy",
         "args": ["http://localhost:8000/mcp"]
       }
     }
   }
   

   Note: Replace YOUR_USERNAME with your actual username.

4. Restart Claude Desktop

You'll see Tool Gating's tools available in Claude, including:

• discover_tools - Find relevant tools based on queries
• provision_tools - Select tools within token budgets
• register_tool - Add new tools to the registry
• list_mcp_servers - View registered MCP servers
• And more!

See MCP Native Usage Guide for detailed instructions.

🔍 API Endpoints

Tool Discovery

POST /api/tools/discover

Discover relevant tools based on semantic search.

Request:

{
  "query": "I need to perform calculations",
  "tags": ["math", "calculation"],
  "limit": 5
}

Response:

{
  "tools": [
    {
      "tool_id": "calculator",
      "name": "Calculator",
      "description": "Perform mathematical calculations",
      "score": 0.95,
      "matched_tags": ["math", "calculation"],
      "estimated_tokens": 50
    }
  ],
  "query_id": "uuid",
  "timestamp": "2024-01-01T00:00:00"
}

Tool Provisioning

POST /api/tools/provision

Select and format tools for LLM consumption with token budget enforcement.

Request:

{
  "tool_ids": ["calculator", "web-search"],
  "max_tools": 3
}

Response:

{
  "tools": [
    {
      "name": "Calculator",
      "description": "Perform mathematical calculations",
      "parameters": { "type": "object", "properties": {...} },
      "token_count": 50
    }
  ],
  "metadata": {
    "total_tokens": 150,
    "gating_applied": true
  }
}

🔄 How It Works

1. Claude Desktop Configuration: Configure only Tool Gating MCP

   {
     "mcpServers": {
       "tool-gating": {
         "command": "mcp-proxy",
         "args": ["http://localhost:8000/mcp"]
       }
     }
   }
   

2. Backend Server Connection: Tool Gating connects to multiple MCP servers

   Tool Gating → puppeteer (browser tools)
              → exa (search tools)
              → context7 (documentation)
              → filesystem (file operations)
   

3. Natural Language Discovery: "I need to search for research papers"

   Claude → discover_tools → Semantic Search → Returns relevant tools
   

4. Real-time Tool Execution: Tools are loaded on-demand

   execute_tool("exa_research_paper_search", {...}) → Validates → Loads → Executes
   

   No provisioning needed! Tools are dynamically loaded when you use them.

🎯 Usage Examples

Running the Demo

# Make sure the server is running first
tool-gating-mcp

# In another terminal, run the interactive demo
python demo.py

Manual Testing

# Test the server endpoints
python test_server.py

Example: Finding Math Tools

import httpx
import asyncio

async def find_math_tools():
    async with httpx.AsyncClient() as client:
        response = await client.post(
            "http://localhost:8000/api/tools/discover",
            json={
                "query": "I need to solve equations",
                "tags": ["math"],
                "limit": 3
            }
        )
        tools = response.json()["tools"]
        print(f"Found {len(tools)} relevant tools")
        for tool in tools:
            print(f"- {tool['name']}: {tool['score']:.3f}")

asyncio.run(find_math_tools())

🧪 Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=tool_gating_mcp

# Run specific test files
pytest tests/test_discovery_service.py -v

# Run integration tests
pytest tests/test_integration.py -v

📊 Architecture Details

1. Tool Registration: Tools are registered with metadata, tags, and token estimates
2. Semantic Search: User queries are embedded using sentence transformers
3. Relevance Scoring: Tools are scored based on:
   • Cosine similarity between query and tool embeddings
   • Tag matches (adds 0.2 boost per matching tag)
4. Real-time Loading: Tools are validated and loaded on-demand during execution
5. MCP Formatting: Selected tools are formatted according to MCP protocol

🔧 Configuration

The system uses sensible defaults but can be configured:

• Max Tokens: Default 2000 tokens per request
• Max Tools: Default 10 tools per request
• Embedding Model: all-MiniLM-L6-v2 (384-dimensional embeddings)

📖 Documentation

• Tool Discovery System - How semantic search and tags work together
• Adding New MCP Servers - Step-by-step guide to integrate new servers
• AI Integration Guide - How AI assistants can automatically add MCP servers
• Architecture Overview - System design and component interactions

📁 Project Structure

tool-gating-mcp/
├── src/
│   └── tool_gating_mcp/
│       ├── __init__.py
│       ├── main.py              # FastAPI application
│       ├── api/
│       │   ├── models.py        # Pydantic models
│       │   ├── tools.py         # Tool management endpoints
│       │   └── mcp.py           # MCP server endpoints
│       ├── models/
│       │   └── tool.py          # Domain models
│       └── services/
│           ├── discovery.py     # Semantic search
│           ├── gating.py        # Tool selection logic
│           └── repository.py    # Tool storage
├── tests/
│   ├── test_*.py               # Test files
│   └── test_integration.py     # Integration tests
├── demo.py                     # Interactive demo
├── test_server.py              # Manual testing script
└── pyproject.toml              # Project configuration

🔌 Integration with MCP Servers

Registering Tools from MCP Servers

# 1. Clear existing demo tools
DELETE /api/tools/clear

# 2. Register tools from your MCP servers
POST /api/tools/register
{
  "id": "exa_research_paper_search",
  "name": "research_paper_search",
  "description": "Search across 100M+ research papers with full text access",
  "tags": ["search", "research", "academic"],
  "estimated_tokens": 250,
  "server": "exa",
  "parameters": {
    "type": "object",
    "properties": {
      "query": {"type": "string"},
      "numResults": {"type": "number", "default": 5}
    },
    "required": ["query"]
  }
}

Using with LLM Orchestration

1. LLM receives user query: "Find recent papers on quantum computing"
2. Orchestrator queries tool gating:

   POST /api/tools/discover
   {"query": "find research papers", "limit": 3}
   

3. System returns relevant tools: Only research tools, not file editors
4. Orchestrator provisions tools:

   POST /api/tools/provision
   {"tool_ids": ["exa_research_paper_search"], "max_tokens": 500}
   

5. LLM executes directly with MCP server: Using the provisioned tool definition

AI-Assisted Server Registration

AI assistants can automatically add new MCP servers:

# User: "Add this Slack MCP server to tool gating"
# AI: Connects to server, discovers tools, and registers everything

POST /api/mcp/ai/register-server
{
  "server_name": "slack",
  "config": {
    "command": "npx",
    "args": ["@slack/mcp-server"],
    "env": {"SLACK_TOKEN": "xoxb-..."}
  },
  "tools": [
    // AI provides all discovered tools with metadata
  ]
}

# Result: Slack server + all tools registered and ready for use

🧑‍💻 Development

Code Quality

# Format code
black .

# Run linter
ruff check . --fix

# Type checking
mypy .

# Run all checks
black . && ruff check . --fix && mypy . && pytest

Adding New Tools

Tools can be added to the repository in services/repository.py:

Tool(
    id="my-tool",
    name="My Tool",
    description="Description for semantic search",
    tags=["category", "function"],
    estimated_tokens=100,
    parameters={
        "type": "object",
        "properties": {...},
        "required": [...]
    }
)

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes and add tests
4. Run quality checks (black, ruff, mypy, pytest)
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built with FastAPI
• Semantic search powered by Sentence Transformers
• MCP protocol integration via fastapi-mcp
• Demo UI using Rich