MCP Server
Allows generating code via the OpenAI API, integrating with large language models for code generation tasks.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Serverwrite 'Hello World' to a file named hello.py"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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/healthDocker
docker-compose up -d
curl http://localhost:8000/healthConfiguration
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 defaultOptional 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/protectedAvailable Tools
Tool | Description |
| Create directories (sandboxed) |
| Write text files |
| Read text files |
| List directory contents |
| Execute shell commands (filtered) |
| Generate code via OpenAI API |
| Generate code via Gemini API |
API Endpoints
GET /health- Health checkGET /metrics- Prometheus metricsPOST /api/auth/login- AuthenticationPOST /mcp/mcp.json/- MCP JSON-RPC (requires auth)POST /api/adapters/{type}- Create data adaptersGET /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-filesProduction Deployment
Set strong
JWT_SECRET(32+ characters)Change default
ADMIN_PASSWORDSet
ENVIRONMENT=productionConfigure appropriate
CORS_ORIGINSUse HTTPS termination at load balancer
Monitor
/healthand/metricsendpoints
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
This server cannot be installed
Maintenance
Resources
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If you are the server author, to access and configure the admin panel.
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