Sports Context Protocol

Name: Sports Context Protocol
Author: Dswane

by Dswaneversion 0.1.2i★ –iRuns on your computer

Registry nameio.github.Dswane/sports-context-protocol

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "sports-context-protocol": {
      "command": "npx",
      "args": [
        "-y",
        "sports-context-protocol"
      ]
    }
  }
}

Run this in your terminal

claude mcp add sports-context-protocol -- npx -y sports-context-protocol

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "sports-context-protocol": {
      "command": "npx",
      "args": [
        "-y",
        "sports-context-protocol"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "sports-context-protocol": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "sports-context-protocol"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account Dswane, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32691 servers in this category
Published to the registry more recently than 10% of the other 32691 servers in this category
Basic checks equal to or better than 100% of the other 32691 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Sports Context Protocol safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.2) reached the registry 6 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Dialyne

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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.

Protocol-level spec: docs/SCP_CORE_SPEC.md
The profile system: docs/SCP_PROFILES.md
This profile: docs/SCP_GOLF_PROFILE.md

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

Tool	What it does
`get_course_context`	Full operating context — read this before acting.
`get_available_inventory`	Available tee times near a preferred time.
`check_booking_action`	Is a booking allowed, blocked, risky? Writes a decision.
`check_pricing_action`	Is a quoted/discounted price allowed? Writes a decision.
`create_soft_hold`	Temporary hold on a tee time before confirmation.
`write_decision_event`	Log a decision directly.
`submit_outcome_feedback`	The learning tool. Feed an outcome back to SCP.
`get_learning_insights`	What SCP has learned.
`explain_action`	Explain 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.

An agent calls a tool. SCP builds a decision fingerprint (a bucketed, deterministic description of the *kind* of decision).
SCP checks rules and learned memory keyed on that fingerprint.
SCP recommends a safe action and logs a decision event.
Feedback arrives via submit_outcome_feedback.
SCP scores the outcome and updates its learning memory.
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

docs/SCP_CORE_SPEC.md — the protocol, sport-agnostic.
docs/SCP_PROFILES.md — the profile system and roadmap.
docs/SCP_GOLF_PROFILE.md — Profile 001 primitive mapping.
docs/SCP_GOLF_SPEC.md — golf implementation detail.
docs/QUICKSTART.md — run and test locally.
docs/DEMO_PROMPTS.md — 10 demo prompts.
docs/LEARNING_LOOP.md — how the learning works.
docs/ROADMAP.md — phases beyond the alpha.

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

Sports Context Protocol

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Dialyne

README from the maker

SCP Golf — Profile 001 of the Sports Context Protocol

Why golf agents need this

What the alpha does

Install

Run

The tools

The resources

The self-learning loop

Docs

Status

License