How to integrate Api sports MCP with CrewAI

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Introduction

This guide walks you through connecting Api sports to CrewAI using the Composio tool router. By the end, you'll have a working Api sports agent that can show today's football fixtures for la liga, get head-to-head record for chelsea vs arsenal, list injured players in premier league this week, fetch starting lineup for tonight's psg match through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Api sports account through Composio's Api sports MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Api sports connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Api sports
  • Build a conversational loop where your agent can execute Api sports operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

What is the Api sports MCP server, and what's possible with it?

The Api sports MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Api sports account. It provides structured and secure access to rich sports data, so your agent can fetch fixtures, analyze team stats, retrieve player info, and explore historical match data on your behalf.

  • Live fixture and match retrieval: Instantly access upcoming and past football fixtures, filterable by league, team, date, or season for up-to-date match information.
  • Detailed match statistics and events: Have your agent pull granular match data, including goals, cards, substitutions, and in-depth statistics like possession, fouls, and passes for any fixture.
  • Team, coach, and player insights: Effortlessly fetch team rosters, coach histories, and individual player statistics or injury reports, making it easy to analyze team lineups and track player performance over time.
  • Head-to-head comparisons and historical data: Ask your agent to compare two teams’ direct matchups, review historical data, or examine league rounds, helping you make informed predictions or reports.
  • Dynamic country and league exploration: Let your agent discover available countries, leagues, and competitions, then drill down by code or search to tailor your sports data queries to specific needs.

Supported Tools & Triggers

Tools
Get CoachesTool to fetch coaches and their career history.
Get CountriesTool to fetch available countries for league queries.
Get FixturesTool to retrieve football fixtures/matches.
Get fixtures eventsTool to get events (goals, cards, substitutions, var, etc.
Get Head-to-Head FixturesTool to get head-to-head fixtures between two teams.
Get Fixture LineupsTool to retrieve starting xi and substitutes for a fixture.
Get fixtures playersTool to get player statistics from a fixture.
Get Fixtures RoundsTool to get the rounds for a league or cup.
Get fixture statisticsTool to get statistics for a fixture.
Get InjuriesTool to get injured or suspended players.
Get LeaguesTool to retrieve leagues and cups.
Get League SeasonsTool to get the list of available seasons for all leagues.
Get OddsTool to fetch pre-match odds.
Get Odds BetsTool to get all available pre-match bet types.
Get Odds BookmakersTool to list all available pre-match bookmakers.
Get In-Play OddsTool to fetch in-play odds for fixtures in progress.
Get Live Odds BetsTool to fetch all available bet types for in-play odds.
Get PlayersTool to get player statistics.
Get Players ProfilesTool to get the list of all available players.
Get Players SeasonsTool to list all available seasons for player statistics.
Get Players TeamsTool to get the list of teams and seasons in which a player played during his career.
Get Players Top AssistsTool to get the 20 best players (top assists) for a league or cup.
Get Players Top ScorersTool to get the 20 best players (top scorers) for a league or cup.
Get Players Top Yellow CardsTool to get the 20 players with the most yellow cards for a league or cup.
Get PredictionsTool to get predictions about a fixture.
Get SidelinedTool to get sidelined information (injuries, suspensions, etc.
Get TeamsTool to retrieve available teams.
Get team statisticsTool to get detailed statistics of a team for a given league and season.
Get TimezoneTool to fetch the complete list of available timezones for fixture queries.
Get TransfersTool to get all available transfers for players and teams.
Get TrophiesTool to get trophies for a player or coach.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Api sports connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

bash
pip install composio crewai crewai-tools python-dotenv
What's happening:
  • composio connects your agent to Api sports via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools includes MCP helpers
  • python-dotenv loads environment variables from .env

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Api sports MCP URL

Create a Composio Tool Router session for Api sports

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["api_sports"],
)
url = session.mcp.url
What's happening:
  • You create a Api sports only session through Composio
  • Composio returns an MCP HTTP URL that exposes Api sports tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Api sports Assistant",
    goal="Help users interact with Api sports through natural language commands",
    backstory=(
        "You are an expert assistant with access to Api sports tools. "
        "You can perform various Api sports operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Api sports MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Api sports operations.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Api sports related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_agent],
        tasks=[task],
        verbose=False,
    )

    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's happening:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_api_sports_agent.py

Complete Code

Here's the complete code to get you started with Api sports and CrewAI:

python
# file: crewai_api_sports_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

def main():
    # Initialize Composio and create a Api sports session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["api_sports"],
    )
    url = session.mcp.url

    # Configure LLM
    llm = LLM(
        model="gpt-5-mini",
        api_key=os.getenv("OPENAI_API_KEY"),
    )

    # Create Api sports assistant agent
    toolkit_agent = Agent(
        role="Api sports Assistant",
        goal="Help users interact with Api sports through natural language commands",
        backstory=(
            "You are an expert assistant with access to Api sports tools. "
            "You can perform various Api sports operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Api sports operations.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Api sports related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Api sports through Composio's Tool Router. The agent can perform Api sports operations through natural language commands. Next steps:
  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations

How to build Api sports MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Api sports MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Api sports MCP with Claude Agent SDK

Google ADK

Google ADK

Use Api sports MCP with Google ADK

LangChain

LangChain

Use Api sports MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Api sports MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Api sports MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Api sports MCP with LlamaIndex

FAQ

What are the differences in Tool Router MCP and Api sports MCP?

With a standalone Api sports MCP server, the agents and LLMs can only access a fixed set of Api sports tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Api sports and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with CrewAI?

Yes, you can. CrewAI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Api sports tools.

Can I manage the permissions and scopes for Api sports while using Tool Router?

Yes, absolutely. You can configure which Api sports scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Api sports data and credentials are handled as safely as possible.

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