How to integrate Api sports MCP with Autogen

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Introduction

This guide walks you through connecting Api sports to AutoGen 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 AutoGen 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 and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Api sports
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Api sports tools
  • Run a live chat loop where you ask the agent to perform Api sports operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Api sports account you can connect to Composio
  • Some basic familiarity with Autogen and Python async

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 python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Api sports via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Api sports connections to use

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async 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
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Api sports tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Api sports assistant agent with MCP tools
    agent = AssistantAgent(
        name="api_sports_assistant",
        description="An AI assistant that helps with Api sports operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Api sports tools from the workbench

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Api sports related question or task to the agent.\n")

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

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

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Api sports tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

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

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async 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 MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Api sports assistant agent with MCP tools
        agent = AssistantAgent(
            name="api_sports_assistant",
            description="An AI assistant that helps with Api sports operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Api sports related question or task to the agent.\n")

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

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

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Api sports through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Api sports, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

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

CrewAI

CrewAI

Use Api sports MCP with CrewAI

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 Autogen?

Yes, you can. Autogen 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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