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Sports Context Protocol

Official registry Games, sports and entertainment

Self-learning operational context layer for AI sports agents. Profile 001: golf.

Source code Website Documentation

Install

Add this to your client's MCP configuration (Claude Desktop: claude_desktop_config.json; Cursor: .cursor/mcp.json; VS Code and others use the same "mcpServers" shape).

NPM package sports-context-protocol
Claude Code 
claude mcp add sports-context-protocol -- npx -y sports-context-protocol@0.1.2
JSON configuration (Claude Desktop, Cursor, VS Code) 
{
    "mcpServers": {
        "sports-context-protocol": {
            "command": "npx",
            "args": [
                "-y",
                "sports-context-protocol@0.1.2"
            ]
        }
    }
}

Documentation

From the project's README on GitHub, refreshed weekly. Links open on GitHub.

Contents
  1. SCP Golf — Profile 001 of the Sports Context Protocol
  2. Why golf agents need this
  3. What the alpha does
  4. Install
  5. Run
  6. The tools
  7. The resources
  8. The self-learning loop
  9. Docs
  10. Status
  11. License

SCP Golf — Profile 001 of the Sports Context Protocol

The context, safety, and memory layer for sports agents. Golf first.

Before a sports agent acts, it checks SCP. Then SCP learns from what happened.

SCP — Sports Context Protocol — is an open context layer for AI agents operating in sports. Every sport venue has the same five things underneath: inventory, rules, actions, consequences, and memory. SCP is the standard way an agent reads those before it acts, and learns from the outcome after.

SCP Golf is Profile 001 — the first working profile. Golf is the cleanest wedge because an agent cannot safely book, price, move, or recommend anything at a course without understanding tee-sheet state, protected inventory, pricing policy, pace risk, events, and operator memory. Golf makes the problem impossible to ignore.

This repository is SCP Golf Alpha: a synthetic demo course, a local MCP server, booking and pricing safety checks, soft holds, a decision ledger, and a self-learning memory. No real course data, no integrations, no database.

Why golf agents need this

AI golf agents are coming — answering calls, booking tee times, quoting prices, moving reservations. The problem: most agents only know the conversation. They do not know the course: the tee-sheet state, the member protections, the league blocks, the pricing floor, the pace risk, the operator's preferences, and what happened the last time a similar decision was made.

SCP Golf gives them that, and then it learns.

What the alpha does

  • Models one synthetic course — Harbor Ridge Golf Club — for Saturday, June 6, 2026: a 67-slot tee sheet with member, league, and outing blocks.
  • Exposes the course as 11 MCP resources (read-only context).
  • Exposes 9 MCP tools for safe booking, pricing, soft holds, decision logging, outcome feedback, and learning insights.
  • Exposes 4 MCP prompts (reusable workflows).
  • Logs every decision to a ledger and learns from outcomes — operator overrides, pace issues, price rejections — so the next similar decision is better.

Install

npm install
npm run build
npm run typecheck
npm run test

Run

npm run dev      # runs the MCP server on stdio (tsx, no build needed)
npm start        # runs the compiled server from dist/

Test it interactively with the MCP Inspector:

npx @modelcontextprotocol/inspector npm run dev

The tools

ToolWhat it does
get_course_contextFull operating context — read this before acting.
get_available_inventoryAvailable tee times near a preferred time.
check_booking_actionIs a booking allowed, blocked, risky? Writes a decision.
check_pricing_actionIs a quoted/discounted price allowed? Writes a decision.
create_soft_holdTemporary hold on a tee time before confirmation.
write_decision_eventLog a decision directly.
submit_outcome_feedbackThe learning tool. Feed an outcome back to SCP.
get_learning_insightsWhat SCP has learned.
explain_actionExplain a result for golfer / operator / developer.

The resources

scp://course/demo and its children: context, tee-sheet, booking-policy, pricing-policy, events, weather, pace, decision-ledger, learning-memory, soft-holds.

The self-learning loop

This is the heart of SCP. It is operational learning — no model training.

  1. An agent calls a tool. SCP builds a decision fingerprint (a bucketed, deterministic description of the kind of decision).
  2. SCP checks rules and learned memory keyed on that fingerprint.
  3. SCP recommends a safe action and logs a decision event.
  4. Feedback arrives via submit_outcome_feedback.
  5. SCP scores the outcome and updates its learning memory.
  6. The next decision with a matching fingerprint is shaped by that memory.

The demo moment: ask for Saturday ~09:00, have an operator override the result once, ask again — SCP now recommends the operator's preferred time. See docs/LEARNING_LOOP.md.

Docs

Status

Alpha. Synthetic data. Booking safety first. Self-learning from decision outcomes. Not partnered with any course, not integrated with any provider, not live with any operator.

License

MIT

Frequently asked questions

What is Sports Context Protocol?

Self-learning operational context layer for AI sports agents. Profile 001: golf.

How do I install Sports Context Protocol?

It runs on your machine. In Claude Code: claude mcp add sports-context-protocol -- npx -y sports-context-protocol@0.1.2. For other clients, paste the JSON configuration from this page into their MCP settings.

Does Sports Context Protocol need an API key?

Its registry entry lists no required key or secret. Some features may still need an account with the service it connects to.

Is Sports Context Protocol open source?

Yes. Its code is on GitHub under the MIT license.

Who maintains Sports Context Protocol?

It is published by Dswane. AI.tn lists it but does not maintain or endorse it.

From the official MCP Registry. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.