MCP-Soccerdata MCP Server

What does “MCP-Soccerdata” MCP Server do?

MCP-Soccerdata is an open-source Model Context Protocol (MCP) server that connects to the SoccerDataAPI to deliver up-to-date football (soccer) match information via natural language interactions. Designed for use with MCP-enabled clients such as Claude Desktop, it allows users and AI assistants to retrieve structured, real-time football data by leveraging large language models (LLMs). The server provides live insights about ongoing matches, match listings, team lineups, key match events, betting odds, and league metadata. This integration enables AI-powered workflows for querying football data, facilitating richer development, research, and fan engagement experiences.

List of Prompts

No prompt templates are explicitly documented in the repository or README.

List of Resources

• Live Match Listings
  Provides a global list of all currently active football matches, including team names, kickoff times, stadium details, and current scores.
• Match Details
  Supplies information about match status (scheduled, in progress, finished), goal breakdowns, and final results.
• Key Match Events
  Delivers structured data on goals, substitutions, yellow/red cards, and penalties for each match.
• Team Lineups
  Gives details on starting XI, bench players, injury status, and team formations.

List of Tools

No explicit tool listing or server.py file details are available in the repository or documentation.

Use Cases of this MCP Server

• Live Football Match Monitoring
  Developers or AI agents can fetch up-to-the-minute data on ongoing football matches worldwide.
• Automated Sports Reporting
  Automatically generate match summaries, event breakdowns, and lineup analyses for media or fan sites.
• Fan Engagement Bots
  Power chatbots that answer questions about live scores, upcoming games, or player stats.
• Betting Analysis Applications
  Integrate real-time odds and match data into betting recommendation or analysis tools.
• League and Tournament Tracking
  Build dashboards to track standings, match outcomes, and statistics across leagues and tournaments.

How to set it up

Windsurf

1. Ensure you have Node.js and Windsurf installed.
2. Locate your Windsurf configuration file (e.g., windsurf.json).
3. Add the MCP-Soccerdata MCP server to the mcpServers section with the following JSON snippet:

   {
     "mcpServers": {
       "soccerdata": {
         "command": "npx",
         "args": ["@yeonupark/mcp-soccer-data@latest"]
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify the server is running and accessible.

Claude

1. Install Claude Desktop and ensure it’s up to date.
2. Open the Claude configuration file (location varies by OS).
3. Add the MCP-Soccerdata server to the mcpServers section:

   {
     "mcpServers": {
       "soccerdata": {
         "command": "npx",
         "args": ["@yeonupark/mcp-soccer-data@latest"]
       }
     }
   }
   

4. Save changes and restart Claude Desktop.
5. Confirm successful connection in the Claude interface.

Cursor

1. Install the Cursor IDE and ensure Node.js is available.
2. Open or create a .cursorconfig file in your workspace.
3. Insert the server configuration:

   {
     "mcpServers": {
       "soccerdata": {
         "command": "npx",
         "args": ["@yeonupark/mcp-soccer-data@latest"]
       }
     }
   }
   

4. Save and reload Cursor.
5. Check that the MCP server appears in the Cursor extensions panel.

Cline

1. Install the Cline terminal client.
2. Open your .cline.json config file.
3. Add the configuration:

   {
     "mcpServers": {
       "soccerdata": {
         "command": "npx",
         "args": ["@yeonupark/mcp-soccer-data@latest"]
       }
     }
   }
   

4. Save and restart Cline.
5. Use the cline mcp list command to verify connection.

Securing API Keys

Store sensitive API keys in environment variables and pass them via the env field in your configuration. Example:

{
  "mcpServers": {
    "soccerdata": {
      "command": "npx",
      "args": ["@yeonupark/mcp-soccer-data@latest"],
      "env": {
        "SOCCERDATA_API_KEY": "${SOCCERDATA_API_KEY}"
      },
      "inputs": {
        "apiKey": "${SOCCERDATA_API_KEY}"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "MCP-Soccerdata": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “MCP-Soccerdata” to the actual name of your MCP server and update the URL accordingly.

Overview

Our opinion

MCP-Soccerdata delivers a focused, real-time football data server with well-described resources and setup instructions. However, the lack of documented prompt templates and explicit tool definitions limits out-of-the-box flexibility and developer adoption for advanced MCP workflows.

MCP Score

Based on the tables above, I would rate this MCP server a 5 out of 10: it offers solid core functionality and documentation for football data, but lacks richer MCP features like prompt templates, tool listings, and clear sampling/roots support for advanced integration.