Python MCP Server Support: Build AI Tools with FastAPI and Python

We're excited to announce full Python support for MCP servers on DeployContext. Now you can build and deploy Model Context Protocol servers using Python, FastAPI, and the official MCP Python SDK.

Why Python?

Python is the language of AI. From machine learning to data science, Python powers the AI ecosystem. Now you can leverage your Python skills to build MCP servers that integrate with Claude and other AI assistants.

Key Benefits

Familiar Stack: Use FastAPI, uvicorn, and the tools you already know
Rich Ecosystem: Access thousands of Python libraries and packages
AI/ML Integration: Connect to PyTorch, TensorFlow, scikit-learn, and more
Automatic Runtime Detection: DeployContext automatically detects Python repos

How It Works

DeployContext automatically detects your project's runtime based on the files in your repository:

requirements.txt → Python
pyproject.toml → Python
package.json → Node.js

When you deploy a Python repository, we detect the runtime, validate the MCP SDK, generate an optimized Dockerfile, and deploy globally.

Quick Start

1. Add your dependencies in requirements.txt:

mcp>=1.0.0
fastapi>=0.115.0
uvicorn[standard]>=0.32.0
sse-starlette>=2.1.0

2. Create your server with the standard MCP endpoints (/health, /sse, /message).

3. Deploy - Push to GitHub and deploy from the dashboard.

👉 **See the complete example repo** for a working Python MCP server with text utilities.

What's Next?

Python MCP support opens up exciting possibilities:

ML Model MCPs - Expose PyTorch/TensorFlow models as tools
Data Pipeline MCPs - Process and analyze data with pandas
Scientific MCPs - Complex calculations and visualizations

Get Started

Questions? Contact support.