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MCP Server Project

A secure Model Context Protocol (MCP) server providing HTTP endpoints for AI agent tool execution. Built with Python 3.12+, Starlette, and FastMCP.

Quick Start

# Clone and setup
git clone https://github.com/sdirishguy/mcp_server_project.git
cd mcp_server_project
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

# Run server
uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload

# Test
curl http://localhost:8000/health

Related MCP server: MCP Toolkit

Docker

docker-compose up -d
curl http://localhost:8000/health

Configuration

Required environment variables:

JWT_SECRET="your-secret-key-32-chars-minimum"    # Required for production
ADMIN_USERNAME="admin"                            # Default admin user
ADMIN_PASSWORD="secure-password"                  # Change from default

Optional configuration:

SERVER_PORT=8000
MCP_BASE_WORKING_DIR="./shared_host_folder"
ENVIRONMENT="development"                         # development|staging|production
ALLOW_ARBITRARY_SHELL_COMMANDS="false"           # Security: disabled by default
CORS_ORIGINS="http://localhost:3000,https://yourdomain.com"

# API Keys for LLM tools
OPENAI_API_KEY="sk-..."
GEMINI_API_KEY="..."

Authentication

Get a token:

curl -X POST http://localhost:8000/api/auth/login \
  -H "Content-Type: application/json" \
  -d '{"username":"admin","password":"admin123"}'

Use token:

curl -H "Authorization: Bearer YOUR_TOKEN" http://localhost:8000/api/protected

Available Tools

Tool

Description

file_system_create_directory

Create directories (sandboxed)

file_system_write_file

Write text files

file_system_read_file

Read text files

file_system_list_directory

List directory contents

execute_shell_command

Execute shell commands (filtered)

llm_generate_code_openai

Generate code via OpenAI API

llm_generate_code_gemini

Generate code via Gemini API

API Endpoints

  • GET /health - Health check

  • GET /metrics - Prometheus metrics

  • POST /api/auth/login - Authentication

  • POST /mcp/mcp.json/ - MCP JSON-RPC (requires auth)

  • POST /api/adapters/{type} - Create data adapters

  • GET /docs - Interactive API documentation

Security Features

  • JWT-based authentication with configurable providers

  • Path traversal prevention for file operations

  • Shell command filtering and sandboxing

  • Rate limiting on authentication endpoints

  • Security headers (HSTS, CSP, etc.)

  • CORS configuration

  • Audit logging for all operations

Development

Run tests:

pytest -q  # 53 passing, 21 skipped (FastMCP lifespan issue)

Testing

Run tests: pytest -q (53 passing, 21 skipped due to FastMCP lifespan integration)

The skipped tests require proper ASGI lifespan management which TestClient doesn't provide by default. Production server works correctly.

Linting:

pre-commit install
pre-commit run --all-files

Production Deployment

  1. Set strong JWT_SECRET (32+ characters)

  2. Change default ADMIN_PASSWORD

  3. Set ENVIRONMENT=production

  4. Configure appropriate CORS_ORIGINS

  5. Use HTTPS termination at load balancer

  6. Monitor /health and /metrics endpoints

See PRODUCTION_READINESS_REPORT.md for detailed checklist.

Architecture

  • FastMCP: Tool execution via Model Context Protocol

  • Starlette: Async web framework with middleware

  • Pydantic: Configuration management and validation

  • Prometheus: Metrics collection

  • JWT: Stateless authentication

  • Audit Logging: Structured event logging

License

MIT

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