jk-mcp-mls

jk-mcp-mls

MCP server that gives Claude live access to Major League Soccer data — teams, matches, standings, rosters, and schedule-strength analytics — via the ESPN public API.

Category
访问服务器

README

jk-mcp-mls

MCP server that gives Claude live access to Major League Soccer data — teams, matches, standings, rosters, and schedule-strength analytics — via the ESPN public API.

CI Badge Coverage Evals Release Python License: MIT


Table of Contents


Overview

AI assistants like Claude are knowledgeable, but they have a hard cutoff date — they cannot tell you today's MLS standings, last night's scores, or which teams are currently in a playoff position. This project fixes that.

It is an MCP server — a plugin that gives Claude direct access to live MLS data: scores, standings, rosters, and derived schedule-strength analytics. Once installed, you can ask Claude natural-language questions about Major League Soccer and get accurate, up-to-date answers. No subscription, no API key, and no programming required to use it.

This is the v1 scaffold — it wraps the ESPN public API only. Richer sources (mlssoccer.com's Opta-powered feed, official CMS award articles, Leagues Cup, U.S. Open Cup, Concacaf Champions Cup) are on the roadmap.


Features

The v1 surface is eleven read-only, idempotent tools split across two tiers.

ESPN-backed (8)

Tool Description
get_teams List all 30 MLS clubs with IDs and abbreviations
get_team Details for a specific team
get_roster Team's active roster — jersey, position, age, citizenship
get_scoreboard Match scores for a single day, a date range, or the current matchweek
get_team_schedule Every match for a team in the current season — past + upcoming
get_match_details One match's full details — score, venue, attendance, goals, cards, subs
get_standings Current standings grouped by Eastern and Western Conferences
get_news Recent MLS news articles

Derived analytics (3)

Pure functions over live standings + team schedules, exposing schedule-strength context the raw table does not.

Tool Description
get_strength_of_schedule Team's average opponent points-per-game across matches already played
get_results_by_opponent_tier Team's W-L-T split across current top / middle / bottom standings tiers
get_adjusted_points_per_game Team's raw PPG alongside an opponent-quality-adjusted PPG

Roadmap

Not in v1; probed and shown to be viable at the ESPN API:

  • Leagues Cup (concacaf.leagues.cup), U.S. Open Cup (usa.open), Concacaf Champions Cup, Campeones Cup
  • Player leaderboards and team season aggregates once a stable MLS Opta feed is identified
  • Award articles via mlssoccer.com CMS
  • Playoff bracket for the MLS Cup Playoffs

Requirements


Installation

git clone https://github.com/jedi-knights/jk-mcp-mls.git
cd jk-mcp-mls
uv sync

Usage

Run the server in stdio mode (the default — used by Claude Code and Claude Desktop):

uv run python -m mls.server

Run in HTTP mode (for networked or deployed access):

MCP_TRANSPORT=streamable-http uv run python -m mls.server

Example prompts

Standings, scores, rosters:

  • Who is leading the MLS Eastern Conference right now?
  • Show me every MLS result from this past weekend.
  • Who is on Atlanta United's roster?
  • When does LAFC play next?

Schedule strength:

  • Which MLS team has played the toughest schedule so far?
  • Show me Atlanta United's record against the current top 5 teams.
  • Compare Inter Miami and Seattle Sounders on adjusted points-per-game.

Configuration

All configuration is via environment variables. None are required for local use.

Variable Default Description
MCP_TRANSPORT stdio Transport mode: stdio or streamable-http
HOST 0.0.0.0 Bind address (HTTP transport only)
PORT 8000 TCP port (HTTP transport only)
MCP_PATH /mcp/mls URL path (HTTP transport only)
API_HOST https://site.api.espn.com ESPN API base URL
LOG_LEVEL INFO DEBUG, INFO, WARNING, or ERROR
MCP_TRACING_ENABLED unset Bootstrap the OpenTelemetry SDK
MCP_AUTH_ENABLED unset Require RS256 bearer tokens on streamable-http
MCP_AUTH_ISSUER_URL unset Auth-server origin (required when auth is on)
MCP_AUTH_RESOURCE_URL unset This server's public URL for the aud claim

Claude Code

Install from your local clone globally so the server is available in every project:

claude mcp add --scope user mls -- uv run --directory /path/to/jk-mcp-mls python -m mls.server

Replace /path/to/jk-mcp-mls with the absolute path to your clone. Verify with claude mcp list.

Drop --scope user to register only for the current project, or commit a .mcp.json to the repo root for collaborators:

{
  "mcpServers": {
    "mls": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/jk-mcp-mls", "python", "-m", "mls.server"]
    }
  }
}

Claude Desktop

Add the following to your Claude Desktop configuration file.

Location:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json
{
  "mcpServers": {
    "mls": {
      "command": "uv",
      "args": [
        "run",
        "--directory", "/path/to/jk-mcp-mls",
        "python", "-m", "mls.server"
      ]
    }
  }
}

If uv is not on Claude Desktop's PATH, use the absolute path (which uv will show it). Fully quit and relaunch Claude Desktop after saving — a window close is not enough.


Docker

Build the image:

docker build -t jk-mcp-mls:latest .

Run in stdio mode (for MCP clients that spawn a subprocess):

docker run -i --rm jk-mcp-mls:latest

Run in HTTP mode:

docker run --rm -p 8000:8000 \
  -e MCP_TRANSPORT=streamable-http \
  jk-mcp-mls:latest

Development

Install

uv sync

Invoke tasks

All common workflows are invoke tasks. Run uv run inv --list to see everything.

Task Alias Description
uv run inv lint inv l Run ruff linter and format check
uv run inv lint --fix inv l --fix Auto-fix lint violations and reformat
uv run inv test inv t Run the full test suite
uv run inv coverage inv v Run tests with coverage report (threshold: 90%)
uv run inv check-complexity inv cc Check cyclomatic complexity (max 7)
uv run inv build inv b Build wheel and sdist into dist/
uv run inv build-image inv bi Build the Docker image
uv run inv clean inv c Remove build and coverage artifacts

Project structure

src/mls/
├── server.py                     # entry point, transport selection, logging setup
├── adapters/
│   ├── inbound/
│   │   ├── mcp_adapter.py        # FastMCP server, health endpoints, tool registration
│   │   ├── formatters.py         # domain → LLM-readable text
│   │   ├── authorization.py      # inbound authz port implementations
│   │   └── tools/
│   │       ├── espn.py           # 8 ESPN-backed tools
│   │       └── analytics.py      # 3 schedule-strength analytics tools
│   └── outbound/
│       ├── espn_adapter.py       # ESPN HTTP client
│       ├── parsers.py            # ESPN JSON → domain models
│       ├── retry_adapter.py      # transient-failure retry decorator
│       └── caching_adapter.py    # in-process TTL cache
├── application/
│   ├── service.py                # MLSService — use cases, orchestration
│   ├── _helpers.py               # input validation
│   └── _analytics_helpers.py     # pure math for schedule-strength tools
├── domain/
│   ├── models.py                 # Team, Match, Standing (with conference), etc.
│   └── exceptions.py             # MLSNotFoundError, UpstreamAPIError
├── ports/
│   ├── inbound.py                # Authorizer protocol
│   └── outbound.py               # MLSAPIPort protocol
├── observability/                # OpenTelemetry bootstrap (opt-in)
└── security/                     # JWKS token verifier

The dependency direction flows inward: adapters → ports → domain. Nothing in domain/ imports from adapters or a framework.


Contributing

  1. Fork the repository and clone your fork
  2. Create a feature branch: git checkout -b feature/your-feature
  3. Make your changes following the existing patterns (hexagonal architecture, TDD, conventional commits)
  4. Verify the full check suite passes: uv run inv lint && uv run inv check-complexity && uv run inv coverage
  5. Open a pull request against main

All CI checks (lint, complexity, tests, coverage ≥ 90%) must pass before merge.


License

MIT — see LICENSE.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选