mabrow05/custom-mcp-server
3.1
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Model Context Protocol Server for LLM Tool Integration. This server provides a standardized way to handle tool connections for LLM-powered applications.
MCP Server
Model Context Protocol Server for LLM Tool Integration. This server provides a standardized way to handle tool connections for LLM-powered applications.
Setup
Prerequisites
- Python 3.12 or higher
- Docker (optional)
Local Development Setup
- Create a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
- Install dependencies:
uv pip install -e .
- Create a
.envfile:
SECRET_KEY=your-secure-secret-key
SEARCH_API_KEY=your-search-api-key # Optional
SEARCH_ENGINE_ID=your-search-engine-id # Optional
- Run the server:
python -m mcp_server.server
Docker Setup
- Build and run with Docker Compose:
docker-compose up --build
The server will be available at http://localhost:8000
API Documentation
Authentication
All endpoints except the token endpoint require authentication using a JWT token.
Get Token
curl -X POST http://localhost:8000/token \
-H "Content-Type: application/x-www-form-urlencoded" \
-d "username=testuser&password=testpass"
Response:
{
"access_token": "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9...",
"token_type": "bearer"
}
Tools API
List Available Tools
curl http://localhost:8000/list_tools \
-H "Authorization: Bearer YOUR_TOKEN_HERE"
Response:
{
"tools": [
{
"name": "web_search",
"description": "Search the web for information",
"type": "web_search",
"parameters": [
{
"name": "query",
"type": "string",
"description": "The search query to find information on the web",
"required": true
},
{
"name": "num_results",
"type": "integer",
"description": "Number of results to return",
"required": false,
"default": 5
}
]
}
]
}
Execute Tool
curl -X POST http://localhost:8000/execute_tool \
-H "Authorization: Bearer YOUR_TOKEN_HERE" \
-H "Content-Type: application/json" \
-d '{
"tool_name": "web_search",
"parameters": {
"query": "example search query",
"num_results": 3
}
}'
Response:
{
"status": "success",
"result": {
"query": "example search query",
"results": [
{
"title": "Example Result",
"snippet": "This is a placeholder for actual search results",
"url": "https://example.com"
}
]
},
"error": null
}
Register New Tool
curl -X POST http://localhost:8000/register_tool \
-H "Authorization: Bearer YOUR_TOKEN_HERE" \
-H "Content-Type: application/json" \
-d '{
"name": "custom_tool",
"description": "A custom tool example",
"type": "function",
"parameters": [
{
"name": "param1",
"type": "string",
"description": "First parameter",
"required": true
},
{
"name": "param2",
"type": "integer",
"description": "Second parameter",
"required": false,
"default": 0
}
]
}'
Response:
{
"status": "success",
"message": "Tool custom_tool registered successfully"
}
Development
Project Structure
mcp-server/
├── src/
│ └── mcp_server/
│ ├── __init__.py
│ └── server.py
├── main.py
├── pyproject.toml
├── Dockerfile
└── docker-compose.yml
Adding New Tools
To add a new tool:
- Define the tool in the server code
- Register it using the
register_toolfunction - Implement the tool's logic in the
execute_toolendpoint
Environment Variables
SECRET_KEY: Secret key for JWT token generationSEARCH_API_KEY: API key for web search (optional)SEARCH_ENGINE_ID: Search engine ID for web search (optional)HOST: Server host (default: 0.0.0.0)PORT: Server port (default: 8000)
Security Notes
- The default user credentials (testuser/testpass) should be changed in production
- Use a strong SECRET_KEY in production
- Implement proper user management and database storage
- Add rate limiting for production use
- Use HTTPS in production
License
MIT
