SteamStats MCP Server

The SteamStats MCP Server bridges AI assistants with the Steam Web API, allowing developers to pull game statistics and user data through a lightweight, JSON‑RPC styled interface. Instead of writing custom API wrappers or handling authentication and rate limiting manually, the server exposes a single endpoint that validates incoming MCP tool calls, forwards them to Steam, and returns clean, typed results. This eliminates boilerplate code in the client and keeps all API‑specific logic encapsulated within the server.

By translating MCP requests into concrete Steam API calls, the server solves a common pain point: how to expose external data sources in a way that an AI assistant can understand and use without exposing raw URLs or credentials. Developers can author high‑level prompts that invoke tools like or , confident that the server will handle parameter validation, error mapping, and response formatting. The result is a declarative workflow where the assistant’s prompt dictates intent while the server guarantees correctness and security.

Key capabilities include:

• Structured command handling – Each MCP tool maps to a specific Steam API endpoint (e.g., ), with arguments validated by Pydantic models.
• Robust error handling – Validation errors, malformed requests, and Steam API failures are all surfaced as clear MCP error responses, preventing silent failures in the assistant.
• Logging and observability – Configurable log levels (DEBUG to CRITICAL) let operators trace request flows and diagnose issues quickly.
• Environment‑driven configuration – The Steam API key, host, and port are injected via environment variables, making the server suitable for containerized or cloud deployments.

Typical use cases span from gaming analytics dashboards that let users ask an assistant “What are my top 5 achievements in Portal 2?” to automated matchmaking tools that query player stats before pairing them. In continuous integration pipelines, the server can validate Steam data as part of test suites, ensuring that game releases or updates reflect correctly in downstream systems. Because the server operates over standard HTTP and JSON, it integrates seamlessly with any MCP‑compliant AI workflow, whether the client is a local tool like Roo or a cloud‑hosted assistant.

Unique advantages of this implementation are its minimal footprint (Python 3.11 + FastAPI) and the clear separation of concerns: the server knows only about Steam, while the AI assistant remains agnostic to external API details. This modularity simplifies maintenance and allows developers to swap out or extend the server with additional Steam endpoints without touching client code.