EdgeFinder MCP Server: Connect AI Agents to Sports Data

Run EdgeFinder as an MCP server so any compatible AI agent can call its sports analysis, odds, schedules, and portfolio tools directly.

EdgeFinder ships with a built-in MCP (Model Context Protocol) server that exposes its full sports analysis capability as a set of callable tools. Instead of running CLI commands yourself, you configure any MCP-compatible client — Claude Desktop, Openclaw, or a custom agent — to spin up the EdgeFinder server on demand and call tools like ask, get_odds, or analyze_position directly in conversation.

What is MCP?

MCP is an open standard that lets AI assistants communicate with external tools over a structured protocol. When you add EdgeFinder to your MCP client config, the client automatically starts the EdgeFinder process, discovers its tools, and can invoke them mid-conversation without you writing any code.

Prerequisites

• Node.js 18 or later
• An EdgeFinder API key — get one at chat.edgefinder.io/settings/integrations
• An MCP-compatible AI client (Claude Desktop, Openclaw, or similar)

Set up the MCP server

1. Get your API key

   Sign in at chat.edgefinder.io/settings/integrations and copy your API key. It starts with ef_live_.

   You can also retrieve a key by running edgefinder login in your terminal if you already have the CLI installed. The login flow saves the key automatically.

2. Add EdgeFinder to your MCP client config

   Open your MCP client’s configuration file and add the edgefinder block under mcpServers. Use npx so you always run the latest version without a global install.

   Claude Desktop

   Open ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows) and add:

   claude_desktop_config.json

   {
     "mcpServers": {
       "edgefinder": {
         "command": "npx",
         "args": ["-y", "@edgefinder/cli", "mcp"],
         "env": {
           "EDGEFINDER_API_KEY": "ef_live_..."
         }
       }
     }
   }
   

   Openclaw

   Add EdgeFinder to your Openclaw MCP server list:

   openclaw config

   {
     "mcpServers": {
       "edgefinder": {
         "command": "npx",
         "args": ["-y", "@edgefinder/cli", "mcp"],
         "env": {
           "EDGEFINDER_API_KEY": "ef_live_..."
         }
       }
     }
   }
   

   Openclaw also supports the native EdgeFinder plugin, which adds slash commands and tighter integration. See the Openclaw integration guide.

   Other MCP clients

   Any MCP-compatible client that accepts a command-style server config works. Use this block:

   mcp config

   {
     "mcpServers": {
       "edgefinder": {
         "command": "npx",
         "args": ["-y", "@edgefinder/cli", "mcp"],
         "env": {
           "EDGEFINDER_API_KEY": "ef_live_..."
         }
       }
     }
   }
   
3. Restart your MCP client

   Save the config file and restart your AI client completely. Most clients do not hot-reload MCP configuration.

   After restarting, you should see EdgeFinder listed in your client’s tool panel or available tool set. If it does not appear, check the troubleshooting section below.

4. Test the connection

   Ask your AI assistant a sports question to confirm EdgeFinder is connected:

   “What does EdgeFinder think about tonight’s NBA games?”

   The client will invoke the ask tool and stream back an AI-powered analysis.

Authentication

EdgeFinder authenticates using an API key passed as an environment variable in your MCP config. Set EDGEFINDER_API_KEY in the env block — never hard-code it elsewhere.

"env": {
  "EDGEFINDER_API_KEY": "ef_live_..."
}

If the server starts without a key configured, it exits immediately with an error message explaining what to set. Your AI client will surface this as a tool error on the first call.

If you see “Authentication failed” during a tool call, your key may be invalid or expired. Generate a fresh key at chat.edgefinder.io/settings/integrations and update the env block in your config.

If you see “Monthly query limit reached,” you have exhausted your plan’s query allowance. Upgrade your subscription at chat.edgefinder.io/subscription to restore access.

Available tools

The MCP server exposes seven tools your AI agent can call. See the MCP tool reference for full parameter documentation.

Tool	Description
ask	AI-powered sports analysis for NFL, NBA, or MLB
get_schedule	Game schedules and live scores for NFL or NBA
get_standings	Current league standings for NFL or NBA
get_odds	Polymarket betting odds for NFL or NBA games
get_portfolio	Polymarket portfolio summary, positions, and trade history
analyze_position	AI analysis of a specific Polymarket position
get_status	Your EdgeFinder account status and query usage

Starting the server manually

You can also start the MCP server directly from the command line, which is useful for testing or building a custom integration:

edgefinder mcp

Or without a global install:

EDGEFINDER_API_KEY=ef_live_... npx -y @edgefinder/cli mcp

The server starts and waits for tool calls. Most users configure this through their MCP client rather than running it directly.