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MCP MLB Stats API

MCP Server

Fast, flexible MLB data access via MCP

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Updated 24 days ago

About

A Model Context Protocol server that wraps the MLB Stats API, enabling quick retrieval of game schedules, results, team stats, and player lookups within an MCP framework.

Capabilities

Resources
Access data sources
Tools
Execute functions
Prompts
Pre-built templates
Sampling
AI model interactions

Quick Overview

The Mcp Mlb Statsapi server turns the raw MLB Stats API into a lightweight, MCP‑ready interface that developers can plug directly into AI assistants. It eliminates the need to write custom HTTP clients, parse JSON responses, or handle pagination for every MLB data request. By exposing a curated set of endpoints—game schedules, results, team performance, and player lookups—developers can query the league’s data in a single, consistent call pattern that AI agents understand natively.

At its core, the server wraps the popular Python library. This means it inherits robust caching, rate‑limit handling, and a well‑tested data model while adding an MCP layer that translates those calls into the structured prompts expected by Claude or other assistants. The result is a smooth, low‑latency experience where an AI assistant can ask for “the score of the latest Dodgers game” and receive a ready‑to‑use response without any intermediate scripting.

Key capabilities include:

  • Game Schedules – retrieve a list of games for any date range, optionally filtered by team or league.
  • Game Results – fetch daily scores, winning and losing teams, and the pitcher of record.
  • Team Results – drill down into a specific team’s most recent game, including play‑by‑play highlights.
  • Player Lookup – resolve player identifiers from partial names, supporting fuzzy matching to handle typos or incomplete input.

These features empower a variety of real‑world workflows. Sports journalists can quickly pull up last‑night’s box score while drafting an article. Fantasy baseball managers can automate daily updates of player stats for their rosters. Developers building chatbots or voice assistants can embed up‑to‑date MLB information without maintaining their own backend services. Because the server follows MCP conventions, any AI client that supports the protocol can tap into it with minimal configuration.

What sets this MCP apart is its blend of simplicity and depth. The server offers a focused set of endpoints that cover the most common MLB use cases, yet it remains extensible—developers can add new queries or modify existing ones by extending the underlying wrapper. The integration with Claude for Desktop further lowers the barrier to entry: a single JSON snippet in the user’s config file launches the server automatically, making it feel like a native feature rather than an external dependency. In short, Mcp Mlb Statsapi delivers real‑time baseball data to AI assistants in a developer‑friendly, protocol‑compliant package that saves time and reduces boilerplate.