Connect Your AI Agent to Live Tennis Data With the MCP Server

What is MCP, in one paragraph

The Model Context Protocol (MCP) is an open standard for connecting AI agents to external tools in a consistent way. Instead of an AI model guessing at tennis facts, or you writing custom glue code against our REST API, your AI agent calls a well-defined tool - like get_tennis_fixtures or get_tennis_head_to_head - and gets back accurate, structured, live data. The tool descriptions themselves teach the model when and how to use each one, so it mostly just works out of the box.

Ultra plan exclusive

Unlike the REST API and WebSocket feed, MCP access is available only on the Ultra plan. A Starter, Pro or Business API key is rejected the moment it tries to connect - not a partial/limited experience, no access at all. If you're not on Ultra yet, see the plans on the homepage.

Connecting your AI client

Most MCP clients (Claude Desktop, Claude Code, Cursor) use the same JSON configuration block:

{
  "mcpServers": {
    "api-tennis": {
      "url": "https://mcp.api-tennis.com/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_ULTRA_APITENNIS_API_KEY"
      }
    }
  }
}

Add that block, restart your client, and you can immediately ask things like "what are today's ATP fixtures at Wimbledon?" or "how has Sinner done against Zverev in their last meetings?" - the client resolves those into the right tool calls on its own. Full setup details are in the MCP documentation.

The 10 tools, briefly

• search_tennis_tournaments - resolve a tournament by name or event type (e.g. "Wimbledon", "Atp Singles").
• get_tennis_fixtures - results and upcoming matches, by date range or by tournament.
• get_live_tennis_matches - what's being played right now.
• get_tennis_standings - the current ATP or WTA tour ranking.
• get_tennis_match_details - set scores, statistics and point-by-point for one match.
• get_tennis_head_to_head - past meetings between two players, plus recent form.
• get_tennis_odds - pre-match bookmaker odds.
• get_live_tennis_odds - in-play odds movement (Business plan or higher).
• get_tennis_player - bio, season stats and tournament history.
• get_tennis_news - AI-synthesized articles tied to the exact player, tournament and match.

Every tool returns compact, model-friendly JSON - a deliberate design choice so an AI agent doesn't burn tokens parsing a raw REST payload it doesn't need. See the full reference for parameters and example responses.

What you can actually build with it

A few concrete directions, beyond just "a chatbot that knows tennis":

• A conversational stats assistant - fans ask natural-language questions ("who's favored tonight, and by how much on serve stats") and get answers pulled from live data, not a stale training set.
• AI-written match previews and recaps - combine get_tennis_fixtures, get_tennis_head_to_head and get_tennis_news so an agent can draft a preview grounded in real head-to-head history and existing coverage, not invented stats.
• A coaching or scouting aide - get_tennis_player's season-by-season, surface-by-surface stats let an agent reason about an opponent's patterns before a match.
• Betting research assistants - get_tennis_odds and get_live_tennis_odds alongside standings and form, so an agent can explain what the market is pricing in and why, not just repeat a number.
• Editorial tooling for tennis media - point an agent at get_tennis_news to summarize or cross-reference AI-synthesized coverage across players and tournaments.

None of this requires you to write REST integration code, handle pagination, or teach the model your data's field names - the MCP tools already speak that language.

How this differs from the REST API and WebSocket feed

Same underlying data, same Ultra plan API key - but shaped for AI agents instead of application code: purpose-built tools instead of raw query parameters, and descriptions that guide the model's tool choice (for example, steering it toward get_tennis_fixtures for an upcoming match and get_tennis_head_to_head only for ones already played). If you're integrating from your own backend rather than an AI agent, the REST API or WebSocket API remain the right tools for that job.

Getting started

Upgrade to Ultra if you haven't already, add the configuration block above to your MCP client, and open the full documentation for every tool's parameters and example responses. If you want the plain-language pitch first, start with the MCP overview page.