Hockey Card Analyst

Turn an advanced hockey card into plain language you can say out loud and defend.

Hockey Card Analyst is a translator between advanced metrics and normal hockey conversation. It reads one analytics model's card for a player and tells you, in words, what the numbers actually say.

The problem it solves

Advanced player cards are everywhere now, but reading one well means knowing the model underneath: what is repeatable versus lucky, what is a real skill versus an artifact of how a player is deployed, why a 95th percentile is not the same as a 99th. Most fans do not have time to learn the methodology, so the metrics get ignored or misread and the argument stays stuck at the eye test. The gap is not access to data. It is interpretation.

It is for the fan who watches the games, trusts what they see, and wants the numbers to back that up or check it.

Related MCP server: NHL MCP Server

Query Example

Let's compare the three Hughes' brothers (using Claude Desktop):

What you can ask it

• A claim gets made about a player ("he's just a net-front guy, can't do more than that") and you want to know whether the data agrees.

• You have an eye-test read and want to see if the metrics confirm it or complicate it.

• A trade gets floated and you want an honest answer to "is X an upgrade on Y."

Why you can trust it

Every answer traces back to a number on the card. It tells you when a claim is half-right, and which half. It admits what the card cannot see instead of bluffing. It takes the noise out and leaves something you can explain to someone else and stand behind.

It interprets one model's read of a player. It does not declare anyone good or bad in some absolute sense.

How it works, under the hood

This is a local MCP server. The model you are talking to (the LLM) reads the card image and handles the language. The server does the deterministic part: it maps each percentile to a tier, grades claims against the numbers, compares two players within the same position pool, attaches the methodology caveats, and decides what the card cannot answer. The reading rules live in config/interpretation.yaml, not in the model's memory, so the answers stay consistent. It works for forwards, defensemen, and goalies.

Three tools, thin wrappers over the engine:

• assess_player(card): overall tier, strengths and weaknesses, deployment, trajectory, caveats, a one-line summary.

• adjudicate_claim(card, assertions): grades each claim supported / partial / not_supported / unverifiable, with the cited number.

• compare_players(card_a, card_b, focus): component by component, an overall edge or an honest split, and a durability flag.

What you need

A hockeystats.com subscription and a player card you have pulled yourself. The tool does not fetch, scrape, or store cards. It only interprets a card you supply, and the LLM reads the card image at runtime, so the server itself only ever sees the numbers.

To run the server you need Python 3.11 or newer (developed and tested on 3.14).

How to run it

This is a standard MCP server that speaks over stdio, so any MCP-capable client can use it. It was built and tested with Claude Desktop on macOS. The same command works for other clients; you just put it in that client's MCP config.

1. Install (one time):

   git clone https://github.com/augforce/hockey-card-analyst.git
   cd hockey-card-analyst
   python3 -m venv .venv
   .venv/bin/python -m pip install fastmcp pydantic PyYAML

2. Register the server with your client. Every MCP client needs the same two things: the command (the venv Python) and one argument (the server script). Use absolute paths for your machine:

   • command: /absolute/path/to/hockey-card-analyst/.venv/bin/python

   • args: ["/absolute/path/to/hockey-card-analyst/src/server.py"]

   Claude Desktop (tested). Edit ~/Library/Application Support/Claude/claude_desktop_config.json (on Windows, %APPDATA%\Claude\claude_desktop_config.json) and merge:

   {
     "mcpServers": {
       "hockey-card-analyst": {
         "command": "/absolute/path/to/hockey-card-analyst/.venv/bin/python",
         "args": ["/absolute/path/to/hockey-card-analyst/src/server.py"]
       }
     }
   }

   Quit Claude Desktop fully and reopen so it reloads the config.

   Gemini CLI. Add the same block under mcpServers in your Gemini settings (~/.gemini/settings.json), then restart the CLI.

   Other stdio clients (Cursor, VS Code, Cline, Continue, the OpenAI Agents SDK, and similar). Each has its own place to register an MCP server, but the entry is the same command and args shown above.

   ChatGPT. ChatGPT's connector support targets remote servers rather than local stdio, so to use it there you would run this server over HTTP instead of stdio (FastMCP supports an HTTP transport; see the FastMCP docs). That path is not tested here.

3. Confirm the loop. Start a conversation, give the model a player card image, and ask in plain language, for example:

   Someone told me this kid is an elite two-way center already. True?

   The model should read the card, route it through the tools, and answer from the numbers: back the offence where the card supports it, push back on the two-way side if the defensive numbers do not, note how the player is deployed, and flag the trajectory.

Scope and sourcing

The tools read the card and nothing else. Every verdict traces back to a percentile on the card, so the whole answer is auditable — you can check each claim against a number you can see.

Team, roster, and contract context — trades, who's on the roster, who leads a team in scoring, recent game results — is out of scope by design. The tools don't fetch it and don't guess it (adjudicate_claim returns such claims as unverifiable). Keeping that context out is what keeps the auditable trail clean. This is the default, no-setup behavior: card-bound and honest.

Optional: let the host model add outside context

If you want the host model to pull outside context (trades, contracts, roster fit) and weave it into the answer, you can opt in with a standing instruction. Put it wherever your host keeps persistent instructions: a system prompt, the host's custom instructions, or a project. In Claude Desktop, for example, that is a Project instruction (Projects, then your project, then instructions); other hosts have their own equivalent. It lives in the host app, not in the MCP config JSON, and not in this repo. Paste this in:

When assessing hockey cards, anything the card itself can't answer (trades,
contracts, current team, roster context, recent stats) may be pulled from the
web. When you do, clearly mark which parts of the answer came from the web
rather than the card, and add a brief note that web-sourced facts should be
verified before being taken at face value. Card-derived verdicts come from the
hockey-card-analyst tools and are traceable to the numbers; web facts are
supporting context, not tool output. Keep the two visibly separate.

With this in place the model labels web-sourced facts separately from card-derived verdicts, so the audit trail stays intact: the card verdicts remain traceable to the numbers, and the outside context is clearly flagged as unverified. Web augmentation is an opt-in you configure and label yourself.

Intellectual property

This repo ships logic and rules, not HockeyStats data. You bring your own card, accessed through your own subscription; the server only interprets a card you already have. Do not scrape, cache, or redistribute HockeyStats cards or their underlying data.

Development

Run the tests:

.venv/bin/python -m pytest

The engine has no network or vision dependencies. It only transforms structured numbers, so the suite is fast and deterministic. See DECISIONS.md for the design rationale behind the reading rules.

Status

v1.0: all three tools for forwards, defensemen, and goalies, served over MCP, and tested end to end on Claude Desktop. See DECISIONS.md for the full build log.