MCP FastAPI Server
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
MCP FastAPI Server
Overview
This project is a scalable FastAPI server for handling Model Control Protocol (MCP) requests. It is designed to route requests to different AI model services (such as code generation and debugging), enforce API key security, and provide rate limiting and logging. The server is modular, extensible, and ready for production or research use.
Related MCP server: Dev Guru
Features
FastAPI-based: High-performance, async Python web server
API Key Security: Protects endpoints with API key authentication
Rate Limiting: Per-key or per-client rate limiting (Redis or in-memory)
Code Generation & Debugging: Specialized endpoints for codegen and debugging models
Extensible Routers: Easily add new model types or endpoints
Comprehensive Logging: Info and error logs for all requests and errors
Health Checks: Endpoints for service and model health
Environment-based Configuration: Uses
.envandconfig.pyfor settings
Project Structure
MCP_SERVER/
├── __init__.py
├── auth.py # API key management and authentication
├── codegen_router.py # Endpoints for code generation
├── config.py # App and environment configuration
├── degubber_router.py # Endpoints for code debugging
├── main.py # FastAPI app setup and router inclusion
├── middleware.py # Custom middleware (rate limiting, logging)
├── models.py # Pydantic models for requests/responses
├── requirements.txt # Python dependencies
├── services.py # Model routing and core logic
├── start_server.py # Production server startup script
├── test_client.py # (Legacy) server startup script
├── api_test_script.py # Script to test all main endpointsSetup & Installation
Clone the repository
Install dependencies:
pip install -r requirements.txt(Optional) Set environment variables in a
.envfile or your shell (seeconfig.pyfor options)
Running the Server
Development:
python start_server.pyThe server will start on
http://localhost:8000by default.API docs available at
http://localhost:8000/docs
API Usage
Authentication
All main endpoints require an API key in the
X-API-Keyheader.Example keys are defined in
auth.py(e.g.,mcp-key-dev-123).
Main Endpoints
GET /— Root infoGET /health— Server healthPOST /mcp— General MCP request (routes to appropriate model)GET /api/v1/codegen/capabilities— Codegen model capabilitiesPOST /api/v1/codegen/generate— Generate code (requireswritepermission)GET /api/v1/codegen/templates— Code templatesGET /api/v1/codegen/health— Codegen healthGET /api/v1/debugger/capabilities— Debugger model capabilitiesPOST /api/v1/debugger/analyze— Analyze code for bugs/errorsGET /api/v1/debugger/best-practices— Coding best practicesGET /api/v1/debugger/health— Debugger health
Example Request (with httpx)
import httpx
headers = {"X-API-Key": "mcp-key-dev-123"}
payload = {
"model": "aiden-7b",
"prompt": "Generate a Python function to add two numbers",
"context": {"language": "python"}
}
resp = httpx.post("http://localhost:8000/api/v1/codegen/generate", headers=headers, json=payload)
print(resp.json())Testing
Use
api_test_script.pyto test all main endpoints automatically:python api_test_script.pyThe script prints status and response for each endpoint.
Extending the Project
Add new models: Implement new handlers in
services.pyand register them inModelRouter.Add new endpoints: Create new routers (see
codegen_router.py,degubber_router.py).Change rate limiting: Update or extend
middleware.py.Change API key logic: Update
auth.py.
Contributing
Fork the repo and submit pull requests.
Please include tests and update documentation for new features.
License
MIT License (or specify your own)
Related MCP Connectors
AI model routing on your own vendor keys: pick the best model per prompt, or route and run it.
OpenAI-compatible LLM gateway: pay per request with an API key, signed receipt for each call.
OpenAI-compatible BYOK LLM gateway: per-task routing, shadow evaluation, promotion gates.
AI routing, memory, guardrails, and governance. Routes across Claude, GPT, Gemini.
Related MCP Servers
AlicenseNot gradedqualityBmaintenanceEnables secure access to Xplainable AI platform capabilities for managing machine learning models, deployments, and preprocessors. Supports both read operations (listing models, deployments) and write operations (deploying models, generating reports) with proper authentication and rate limiting.1MIT- FlicenseNot gradedqualityDmaintenanceAn AI-powered code consultation server that routes programming queries to specific AI models based on requested expertise levels. It enables users to receive structured feedback on debugging, architectural decisions, and code reviews from Gemini, Claude, or GPT.-

Adorbis AI MCP Serverofficial
AlicenseNot gradedqualityDmaintenanceEnables AI assistants to access 40+ models via one API key with automatic routing for chat, code, reasoning, and writing tasks.52 npm1MIT- FlicenseNot gradedqualityBmaintenanceEnables high-throughput API rate limiting with deterministic token bucket throttling and burst quota management for AI agents.8-