NFL Transactions MCP

NFL Transactions MCP

A Modular Command-line Program for fetching and filtering NFL transaction data, including player movements, injuries, disciplinary actions, and more from ProSportsTransactions.com.

Category
访问服务器

README

NFL Transactions MCP

A Modular Command-line Program (MCP) for scraping NFL transaction data from ProSportsTransactions.com.

Features

  • Fetch NFL transactions with flexible filtering options:
    • Player/Coach/Executive movement (trades, free agent signings, draft picks, etc.)
    • Movements to/from injured reserve
    • Movements to and from minor leagues (NFL Europe)
    • Missed games due to injuries
    • Missed games due to personal reasons
    • Disciplinary actions (suspensions, fines, etc.)
    • Legal/Criminal incidents
  • Filter by team, player, date range, and transaction type
  • Output data in CSV, JSON, or DataFrame format
  • List all NFL teams and transaction types

Installation

# Clone the repository
git clone <repository-url>
cd nfl_transactions_mcp

# Install requirements
pip install -r requirements.txt

Usage with Cursor

To use this MCP with Cursor, add the following configuration to your .cursor/mcp.json file:

{
  "mcpServers": {
    "nfl-transactions": {
      "command": "python server.py",
      "env": {}
    }
  }
}

Running the MCP Directly

# Run the MCP server via Cursor
cursor run-mcp nfl-transactions

Available Tools

1. fetch_transactions

Fetches NFL transactions based on specified filters.

Parameters:

  • start_date (required): Start date in YYYY-MM-DD format
  • end_date (required): End date in YYYY-MM-DD format
  • transaction_type (optional, default: "All"): Type of transaction to filter
  • team (optional): Team name
  • player (optional): Player name
  • output_format (optional, default: "json"): Output format (csv, json, or dataframe)

Example:

{
  "jsonrpc": "2.0",
  "method": "fetch_transactions",
  "params": {
    "start_date": "2023-01-01",
    "end_date": "2023-12-31",
    "transaction_type": "Injury",
    "team": "Patriots"
  },
  "id": 1
}

2. list_teams

Lists all NFL teams available for filtering.

Example:

{
  "jsonrpc": "2.0",
  "method": "list_teams",
  "id": 2
}

3. list_transaction_types

Lists all transaction types available for filtering.

Example:

{
  "jsonrpc": "2.0",
  "method": "list_transaction_types",
  "id": 3
}

Integration with Super Agents

This MCP is designed to be easily integrated with AI agents or super agents. An agent can make JSON-RPC requests to interact with this MCP and retrieve NFL transaction data based on user queries.

Example agent integration:

# Example of an agent calling the MCP
import json
import subprocess

def call_mcp(method, params=None):
    request = {
        "jsonrpc": "2.0",
        "method": method,
        "params": params or {},
        "id": 1
    }
    
    # Call the MCP via cursor
    cmd = ["cursor", "run-mcp", "nfl-transactions"]
    proc = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE, text=True)
    
    # Send the request and get the response
    response, _ = proc.communicate(json.dumps(request))
    return json.loads(response)

# Example: Get Patriots injury transactions from 2023
result = call_mcp("fetch_transactions", {
    "start_date": "2023-01-01",
    "end_date": "2023-12-31",
    "transaction_type": "Injury",
    "team": "Patriots"
})

print(f"Found {len(result['data'])} transactions")

License

MIT 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