NFL Transactions MCP
Provides Python-based integration with example code for agents to fetch NFL transaction data programmatically.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@NFL Transactions MCPshow me all Patriots injury transactions from last season"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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
Related MCP server: clinicaltrialsgov-mcp-server
Installation
# Clone the repository
git clone <repository-url>
cd nfl_transactions_mcp
# Install requirements
pip install -r requirements.txtUsage 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-transactionsAvailable Tools
1. fetch_transactions
Fetches NFL transactions based on specified filters.
Parameters:
start_date(required): Start date in YYYY-MM-DD formatend_date(required): End date in YYYY-MM-DD formattransaction_type(optional, default: "All"): Type of transaction to filterteam(optional): Team nameplayer(optional): Player nameoutput_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
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