FPL Context

fpl-context-mcp

An MCP server that gives any MCP-capable AI agent live Fantasy Premier League (FPL) data: the current gameweek and deadline, fixtures with difficulty ratings, player stats and match history, any manager's squad and chips, expected points, and injury and suspension flags. It works with zero setup and needs no database: it reads the public FPL API directly. It runs locally in Claude Desktop, Claude Code, Cursor, VS Code Copilot, Windsurf, Gemini CLI and Codex; ChatGPT and other clients that only accept a URL can connect when you host it over HTTP.

Plus three prompts (captain_pick, transfer_plan, chip_timing) that show up as slash commands in clients that support them, and a reference resource (fpl://glossary). See Prompts and resources.

Data limits. FPL serves match-by-match data for the current season only; past seasons are season totals. Questions like "how did X do against Arsenal in earlier seasons" can't be answered from FPL's data, and the tools say so rather than guess.

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Contents

• Quickstart
• Installation
• Configuration
• Registering with Claude Desktop
• Other AI clients (local)
• Remote access over HTTP (ChatGPT and other URL-only clients)
• Running the server standalone
• Verifying connectivity (--check)
• MCP tools reference
• Prompts and resources
• Running tests
• Data sources and disclaimer
• License

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Quickstart

uvx fpl-context-mcp

Register it with your client (Claude Desktop config: {"mcpServers": {"fpl-context": {"command": "uvx", "args": ["fpl-context-mcp"]}}}) and ask *"who should I captain this week?"* or *"show Arsenal's next five fixtures"*. That's it: no database, no account, no API key.

For other clients see Other AI clients. For ChatGPT and other URL-only clients, host it over HTTP.

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Installation

Needs Python 3.11+ and outbound HTTPS access to fantasy.premierleague.com.

Without installing (uv)

uvx fpl-context-mcp runs it straight from PyPI. Requires uv.

From PyPI

pip install fpl-context-mcp

This installs the fpl-context-mcp command.

From source

git clone https://github.com/sbanthia92/fpl-context-mcp

cd fpl-context-mcp pip install -e ".[dev]"

As a dependency of another project

fpl-context-mcp @ git+https://github.com/sbanthia92/fpl-context-mcp.git

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Configuration

The server needs no configuration over stdio. Everything below applies only to the HTTP transport (--transport http), and can be set as environment variables or in a .env file (see .env.example):

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Registering with Claude Desktop

Add the server to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows).

With uvx (recommended)

{

"mcpServers": { "fpl-context": { "command": "uvx", "args": ["fpl-context-mcp"] } } }

If installed with pip

{

"mcpServers": { "fpl-context": { "command": "fpl-context-mcp" } } }

If running from source

{

"mcpServers": { "fpl-context": { "command": "python", "args": ["/absolute/path/to/fpl-context-mcp/server.py"] } } }

Tip: If you use uv, replace "python" with "uv" and prepend "run" to args:

`json

"command": "uv",

"args": ["run", "/absolute/path/to/fpl-context-mcp/server.py"]

`

Restart Claude Desktop. You should see fpl-context appear in the tools panel.

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Other AI clients (local)

Any client that can launch a local MCP server (stdio) works the same way: run the fpl-context-mcp command (no configuration needed).

Claude Code

claude mcp add fpl-context -- uvx fpl-context-mcp

Cursor (~/.cursor/mcp.json), Windsurf (~/.codeium/windsurf/mcp_config.json) and Gemini CLI (~/.gemini/settings.json) all use the same mcpServers shape as Claude Desktop:

{

"mcpServers": { "fpl-context": { "command": "uvx", "args": ["fpl-context-mcp"] } } }

VS Code (Copilot agent mode) — .vscode/mcp.json in your workspace:

{

"servers": { "fpl-context": { "type": "stdio", "command": "uvx", "args": ["fpl-context-mcp"] } } }

OpenAI Codex CLI — ~/.codex/config.toml:

[mcp_servers.fpl-context]

command = "uvx" args = ["fpl-context-mcp"]

uv not installed? pip install fpl-context-mcp, then use "command": "fpl-context-mcp" (no args) in any of the configs above.

Your own agent code — the MCP SDKs (Python, TypeScript) and agent frameworks such as the OpenAI Agents SDK can launch fpl-context-mcp as a stdio server, or connect to it over HTTP as below.

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Remote access over HTTP (ChatGPT and other URL-only clients)

Some clients can't launch a local process — they only accept a server URL. That includes ChatGPT (Settings → Apps & Connectors → Advanced → Developer mode → create a connector) and custom connectors on claude.ai. For these, run the server with the streamable HTTP transport on a machine with a public HTTPS address:

MCP_AUTH_TOKEN=some-long-random-string \

fpl-context-mcp --transport http --host 0.0.0.0 --port 8000

• The MCP endpoint is https:// /mcp; GET /health returns ok for load-balancer and platform health checks.
• --transport, --host and --port can also be set with MCP_TRANSPORT, MCP_HOST and MCP_PORT (or the PORT variable that Render, Cloud Run, Heroku and Fly set).
• The server listens on plain HTTP. Put it behind something that terminates TLS — any of those platforms does, or cloudflared tunnel / ngrok for a quick test from your own machine.
• The default bind address is 127.0.0.1, so nothing is exposed until you pass --host 0.0.0.0.

Authentication. With MCP_AUTH_TOKEN set, every request to /mcp must send Authorization: Bearer ; others get HTTP 401. Clients that let you set headers can use it — for example Claude Code:

claude mcp add --transport http fpl-context https://your-host/mcp --header "Authorization: Bearer some-long-random-string"

and the OpenAI Agents SDK / Responses API MCP tool (headers={"Authorization": "Bearer ..."}).

Several keys. MCP_AUTH_TOKEN accepts a comma-separated list (key-alice,key-bob). Each user can have their own key, and removing one from the list revokes it without touching the others. Rate limits are applied per key.

Rate limiting and load. Set these before exposing a server publicly:

ChatGPT and claude.ai connectors only support OAuth or no authentication — not a static bearer token. To use them you currently have to leave MCP_AUTH_TOKEN unset (the endpoint is then open to anyone who finds the URL) or put an OAuth-capable proxy in front. If you run it open, anyone who finds the URL can use it and spend your server's FPL API traffic, so set the rate limit below. The server logs a warning at startup when it's bound to a non-local address without a token or a rate limit.

Running a public endpoint? See the hosting guide: container, proxy setup, auth modes, and a canary that checks the endpoint every 15 minutes.

Container. A Dockerfile is included: docker build -t fpl-context-mcp ., then run it (optionally with MCP_AUTH_TOKEN set). It runs as a non-root user, listens on $MCP_PORT / $PORT / 8000, defaults to a rate limit of 120 requests a minute, and has a health check on /health.

Where the data comes from. A hosted server calls the public FPL API on behalf of whoever connects, and caches responses in memory. There is nothing to provision or keep fresh. Because you're now serving results to other people, see Data sources and disclaimer, and the hosting guide.

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Running the server standalone

fpl-context-mcp          # installed from PyPI

python server.py # from source

By default the server communicates over stdio: it is designed to be launched by an MCP client, and running it directly is mainly useful for smoke-testing. To run it as a persistent network service, use --transport http (see Remote access over HTTP).

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Verifying connectivity (--check)

fpl-context-mcp --check

Confirms the FPL API is reachable from your machine and exits 0 on success, 1 on failure:

=== fpl-context-mcp check ===

✅ FPL API reachable (700 players, 20 teams)

✅ OK

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MCP tools reference

Live tools overview

These tools call the public FPL API directly and need no database or configuration. They share one cache: FPL's `bootstrap-