Riot MCP Server Integration

What does “Riot” MCP Server do?

MCP-Riot is a community-developed Model Context Protocol (MCP) server that integrates with the Riot Games API to provide League of Legends data to AI assistants via natural language queries. Its primary function is to bridge AI models and the rich dataset offered by Riot Games, empowering assistants to retrieve player information, ranked statistics, champion mastery, and recent match summaries. By exposing these endpoints through the MCP interface, the Riot MCP Server enables developers to build AI-powered tools, bots, or workflows that can interact with League of Legends data seamlessly. This facilitates a new class of applications where AI can answer gameplay questions, analyze player performance, or automate game-related queries—all by leveraging the Riot Games API in a standardized, extensible manner.

List of Prompts

No prompt templates were found in the provided repository files or documentation.

List of Resources

No explicit MCP resources were detailed in the repository files or documentation.

List of Tools

No tools were listed in the visible files or documentation (e.g., no server.py or tool definitions provided).

Use Cases of this MCP Server

• Player Information Retrieval: AI assistants can fetch detailed data about any League of Legends player, such as their summoner name, profile icon, and level, allowing developers to build bots or dashboards that surface player profiles.
• Ranked Stats Access: Retrieve up-to-date ranked statistics for players, enabling analysis of performance trends, rank progression, or competitive standing over time.
• Champion Mastery Analysis: Obtain information about a player’s mastery with specific champions, which can power coaching tools, champion suggestion engines, or gameplay review assistants.
• Recent Match Summaries: Summarize and analyze the latest matches for a player, helping users or teams review game history and strategize for future play.
• Integration with AI Chatbots: Enhance chatbots in Discord, Slack, or other platforms to answer natural language queries about League of Legends stats and history by hooking into the MCP-Riot server.

How to set it up

Windsurf

1. Ensure Node.js is installed and your Windsurf environment is set up.
2. Locate your Windsurf configuration file (usually windsurf.config.json).
3. Add the Riot MCP Server using the following JSON snippet in the mcpServers section:

   {
     "riot-mcp": {
       "command": "npx",
       "args": ["@riot/mcp-server@latest"]
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify setup by checking the MCP server connection in Windsurf.

Securing API Keys (Example)

{
  "riot-mcp": {
    "command": "npx",
    "args": ["@riot/mcp-server@latest"],
    "env": {
      "RIOT_API_KEY": "${RIOT_API_KEY}"
    },
    "inputs": {
      "region": "na1"
    }
  }
}

Claude

1. Confirm Node.js is installed and Claude is configured.
2. Open Claude’s configuration file.
3. In the mcpServers section, add:

   {
     "riot-mcp": {
       "command": "npx",
       "args": ["@riot/mcp-server@latest"]
     }
   }
   

4. Save changes and restart Claude.
5. Check MCP server connectivity in Claude.

Cursor

1. Make sure Node.js is set up and Cursor is ready.
2. Edit the Cursor configuration file.
3. Add this under mcpServers:

   {
     "riot-mcp": {
       "command": "npx",
       "args": ["@riot/mcp-server@latest"]
     }
   }
   

4. Save and restart Cursor.
5. Confirm the MCP server is running in Cursor.

Cline

1. Prepare your environment with Node.js and a configured Cline setup.
2. Open the Cline configuration file.
3. Insert the following into the mcpServers:

   {
     "riot-mcp": {
       "command": "npx",
       "args": ["@riot/mcp-server@latest"]
     }
   }
   

4. Save and restart Cline.
5. Verify that the Riot MCP Server is available.

Note: Always secure your Riot Games API key using environment variables as shown in the Windsurf example above.

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:

{
  "riot-mcp": {
    "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 “riot-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

The MCP-Riot server provides a clear integration between the Riot Games API and AI workflows, and has open licensing, but its documentation and codebase currently lack explicit prompt, resource, and tool definitions. Setup instructions are generic but complete for common platforms. The project is functional and promising for League of Legends AI applications, but would benefit from clearer MCP resource and tool descriptions.

Based on the two tables, I would rate this MCP server a 4 out of 10 for completeness and developer friendliness.

MCP Score