bet-mcp

MCP server (FastMCP + TypeScript) that focuses on Serie A pre-match analysis:

• fixtures_list – next fixtures in a configurable window
• match_snapshot – last 5 results, standings, GF/GA averages
• odds_prematch – normalized odds for 1X2 / OU 2.5 / BTTS across bookmakers
• fair_compute – Poisson-lite probabilities + fair odds
• value_detect – top value picks by comparing best odds vs fair model

Getting started

1. Install dependencies (Node 20+ recommended):

   npm install
   

2. Copy .env.example to .env and provide your keys:

   cp .env.example .env
   # edit the file with FOOTBALL_DATA_TOKEN and ODDS_API_KEY
   

3. Run locally:

   # stdio (Claude Desktop / terminal)
   npm run dev
   
   # or HTTP transport for remote testing
   MCP_TRANSPORT=http PORT=8080 npm run dev
   

4. Build for production:

   npm run build
   npm start
   

5. Deploy on glama.ai:

Glama uses the included glama.yaml/glama.json files to run npm install && npm run build, then starts the server with MCP_TRANSPORT=http on port 8080. Configure FOOTBALL_DATA_TOKEN and ODDS_API_KEY (others optional) in the Glama dashboard so inspections and tool detection can succeed.

Implementation notes

• Stack – FastMCP + Axios + Zod, TypeScript strict mode.
• API clients – Football-Data.org for fixtures/stats; The Odds API for consolidated odds.
• Modeling – Poisson using GF/GA averages + configurable home advantage, derived OU/BTTS probs.
• Caching – In-memory TTL cache to reduce API calls (configurable via CACHE_TTL_SECONDS).
• Value picks – Filters by edge >= 5% and odds >= 1.50, returns rationale referencing λ/form.

Environment variables

Testing

Use npx fastmcp dev src/index.ts or npx fastmcp inspect src/index.ts after installing dependencies to interactively test the tools.