mcpField
M
Mcp_mlb_statsapi

Mcp_mlb_statsapi

Local
github.com/mpizzaData & Analytics7Updated 58w ago

About Mcp_mlb_statsapi

Mcp_mlb_statsapi is a Python MCP server that wraps the MLB Stats API for people and applications querying baseball data. It provides schedule, game and team results, plus fuzzy player lookup.

Key features

  1. 01Game schedules by date and team
  2. 02Daily results with scores and pitchers
  3. 03Recent team game details and highlights
  4. 04Fuzzy player lookup

Use cases

  1. 01Retrieve MLB schedules for a date range
  2. 02Check daily game scores and winning pitchers
  3. 03Review a team's latest game and scoring plays
  4. 04Find a player ID from a name

MCP support

The server exposes MLB Stats API data through MCP tools, including schedules, results, team details, and player lookup.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

baseball-mcp: An MLB Stats API Wrapper for MCP

This project provides an MCP (Model Context Protocol) that acts as a wrapper for the MLB Stats API. It allows you to easily access and process various MLB data points, including schedules, game results, and team information. This server is designed for efficient data retrieval and processing within an MCP framework.

Demo Videos

Here are a couple of demo videos showcasing the capabilities of mcp_mlb_statsapi:

  • Demo 1: MCP MLB Stats API - Quick Overview
  • Quick Overview
  • Demo 2: MCP MLB Stats API - 輸入中文也可以
  • 輸入中文也可以

Features

  • Game Schedules: Retrieve MLB game schedules for specified date ranges, optionally filtering by team.
  • Game Results: Fetch daily game results, including scores, winning/losing teams, and winning pitcher.
  • Team Results: Get detailed results for a specific team's most recent game, including scoring plays and highlights.
  • Player Lookup: Look up player IDs using last name, first name, or a combination of both. Supports fuzzy matching.

Installation

Prerequisites
  • Python 3.10 or newer
  • uv package manager:

If you're on Mac, please install uv as

brew install uv

On Windows

powershell -c "irm https://astral.sh/uv/install.ps1 | iex" 

Otherwise installation instructions are on their website: Install uv

Install via PyPI

The mcp_mlb_statsapi package is available on PyPI and can be installed using pip:

pip install mcp_mlb_statsapi
Install via Github

or you can clone this repo, run it with soruce code.

uv venv
source .venv/bin/activate
uv pip install -r requirements.txt
Claude for Desktop Integration

Go to Claude > Settings > Developer > Edit Config > claude_desktop_config.json to include the following:

    {
      "mcpServers": {
        "mcp_mlb_statsapi": {
           "command": "{YOUR_PYTHON_EXECUTABLE_PATH}/python",
            "args": ["-m",
            "mcp_mlb_statsapi"]
          }
        }
    }

If you install it via source code

{
  "mcpServers": {
    "mcp_mlb_statsapi": {
        "command": "{YOUR_UV_EXECUTABLE_PATH}/uv",
        "args": [
            "--directory",
            "{YOUR_PROJECT_PATH}/src/mcp_mlb_statsapi",
            "run",
            "mcp_mlb_statsapi"
        ]
    }
  }
}

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.

References

This project utilizes the following external libraries and resources:

MseeP.ai Security Assessment Badge Verified on MseeP

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.