MCP has official SDKs for multiple languages, but for data engineering work, Python and TypeScript are the practical choices. The other SDKs (Go, Rust, Java, C#) exist and are production-ready, but the ecosystem tooling and library support for data work heavily favors these two.
Python SDK
For data engineering teams working in Python, this is the primary option.
- Package:
mcpon PyPI - Repository: github.com/modelcontextprotocol/python-sdk
- Current version: v1.25.0 (v2 in development)
- Requirements: Python 3.10+
Installation with uv (recommended):
uv add "mcp[cli]"Or with pip:
pip install "mcp[cli]"The [cli] extra includes command-line tools for testing and installation — you want these during development even if you strip them in production.
The SDK includes FastMCP, a high-level framework that uses decorators to define tools, resources, and prompts. FastMCP handles JSON-RPC serialization, transport management, and protocol negotiation automatically. You write Python functions with type hints and docstrings; FastMCP turns them into MCP tools. See FastMCP Server Skeleton for the hands-on walkthrough.
The Python SDK’s biggest advantage for data engineering is library access. You can import pandas, SQLAlchemy, boto3, the BigQuery client library, Airflow’s API client — whatever your data stack uses. Your MCP tools are just Python functions, so they can call anything Python can call.
TypeScript SDK
For teams with Node.js infrastructure, or frontend developers branching into data tooling:
- Packages:
@modelcontextprotocol/server,@modelcontextprotocol/client - Repository: github.com/modelcontextprotocol/typescript-sdk
- Requirements: Node.js 18+ (22.7.5+ recommended)
Installation:
npm install @modelcontextprotocol/server zodThe TypeScript SDK uses Zod for schema validation, which gives you solid type safety and IDE support. Tool input schemas are defined with Zod types instead of Python type hints, and the schema validation happens at the SDK level before your handler code runs.
TypeScript is a strong choice for async I/O heavy workloads — if your server needs to make many concurrent API calls or handle streaming data, Node’s event loop model works well. It also makes sense when your MCP server is part of a larger Node.js service architecture.
Decision Framework
| Consideration | Python | TypeScript |
|---|---|---|
| Existing codebase | Data pipelines, notebooks, dbt | Web apps, Node services |
| Libraries | pandas, SQLAlchemy, boto3 | Better for async I/O heavy workloads |
| Team expertise | Data engineers | Full-stack developers |
| Deployment | Works with uvx, easy to distribute | npm packages, containerized |
| Schema validation | Type hints + Pydantic | Zod schemas |
The decision usually comes down to one question: what does your team already write? If your data pipelines are in Python, your notebooks are in Python, and your team thinks in Python — use Python. You’ll reuse existing code, existing libraries, and existing mental models.
If your infrastructure is Node-based, or your team is full-stack JavaScript, TypeScript avoids a language boundary. But for most data engineering work, Python with FastMCP is the fastest path to a working server.
Distribution Considerations
How you distribute the server matters too.
Python servers work well with uvx for zero-install execution — users run uvx your-package and it just works. The uv run command handles virtual environment creation and dependency resolution automatically. For internal distribution, publishing to a private PyPI index or distributing as a Git-installable package both work.
TypeScript servers distribute as npm packages or Docker containers. The npx command provides a similar zero-install experience to uvx. For teams already publishing npm packages, this fits naturally into existing CI/CD.
Both languages containerize easily. For shared team servers that run as HTTP services (see MCP Transport Configuration), the choice of language matters less — the server is behind an HTTP endpoint either way.
One Team, One SDK
Avoid mixing SDKs within a team unless there’s a compelling reason. Standardizing on one SDK means shared knowledge, shared debugging techniques, shared deployment patterns. If your team builds five custom MCP servers, all five should use the same SDK so anyone can maintain any server.
The exception is when you’re wrapping a library that only exists in one language. If a critical internal tool has a TypeScript client but no Python equivalent, writing that one server in TypeScript while keeping the rest in Python is a reasonable pragmatic choice.