How to integrate Stack exchange MCP with CrewAI

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Introduction

This guide walks you through connecting Stack exchange to CrewAI using the Composio tool router. By the end, you'll have a working Stack exchange agent that can find top-voted javascript questions this week, list all collectives on stack overflow, show recent comments on my math stackexchange post, get edit history for a specific question through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Stack exchange account through Composio's Stack exchange 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 Stack exchange connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Stack exchange
  • Build a conversational loop where your agent can execute Stack exchange 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 Stack exchange MCP server, and what's possible with it?

The Stack exchange MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Stack Exchange account. It provides structured and secure access to your Stack Exchange activity, so your agent can perform actions like searching questions, exploring collectives, retrieving comments, and analyzing post revisions on your behalf.

  • Powerful question search and discovery: Instantly ask your agent to search for Stack Exchange questions by keyword or tag, helping you find relevant discussions and solutions faster.
  • Collectives and tags exploration: Have your agent retrieve lists of collectives and their associated tags, making it easy to discover expert communities and filter content by topic.
  • Comment management and flagging: Let your agent fetch comments from specific sites and check valid flag options, streamlining comment moderation and participation.
  • Revision history analysis: Direct your agent to retrieve and review the full edit history of posts, enabling transparent audits or understanding how answers evolve over time.
  • Site discovery and navigation: Use your agent to list all available Stack Exchange sites and their API parameters, ensuring you always target the right community for your needs.

Supported Tools & Triggers

Tools
Get Collective QuestionsTool to retrieve questions associated with the specified collective.
Get CollectivesTool to retrieve all collectives on a stack exchange site in alphabetical order.
Get Collective TagsTool to retrieve tags associated with specified collectives.
Get Comment Flag OptionsTool to fetch valid flag options for a given comment.
Get CommentsTool to retrieve comments from a specified stack exchange site.
Get Revisions By IDsTool to retrieve revisions for specific posts by their ids.
Search QuestionsTool to search the site for questions meeting certain criteria.
Get SitesTool to retrieve all stack exchange sites and their api site parameter values.

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 Stack exchange 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 Stack exchange 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 Stack exchange MCP URL

Create a Composio Tool Router session for Stack exchange

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["stack_exchange"],
)
url = session.mcp.url
What's happening:
  • You create a Stack exchange only session through Composio
  • Composio returns an MCP HTTP URL that exposes Stack exchange 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="Stack exchange Assistant",
    goal="Help users interact with Stack exchange through natural language commands",
    backstory=(
        "You are an expert assistant with access to Stack exchange tools. "
        "You can perform various Stack exchange 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 Stack exchange 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 Stack exchange 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 Stack exchange 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_stack_exchange_agent.py

Complete Code

Here's the complete code to get you started with Stack exchange and CrewAI:

python
# file: crewai_stack_exchange_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 Stack exchange session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["stack_exchange"],
    )
    url = session.mcp.url

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

    # Create Stack exchange assistant agent
    toolkit_agent = Agent(
        role="Stack exchange Assistant",
        goal="Help users interact with Stack exchange through natural language commands",
        backstory=(
            "You are an expert assistant with access to Stack exchange tools. "
            "You can perform various Stack exchange 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 Stack exchange 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 Stack exchange 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 Stack exchange through Composio's Tool Router. The agent can perform Stack exchange 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 Stack exchange MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Stack exchange MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Stack exchange MCP with Claude Agent SDK

Google ADK

Google ADK

Use Stack exchange MCP with Google ADK

LangChain

LangChain

Use Stack exchange MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Stack exchange MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Stack exchange MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Stack exchange MCP with LlamaIndex

FAQ

What are the differences in Tool Router MCP and Stack exchange MCP?

With a standalone Stack exchange MCP server, the agents and LLMs can only access a fixed set of Stack exchange tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Stack exchange 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 Stack exchange tools.

Can I manage the permissions and scopes for Stack exchange while using Tool Router?

Yes, absolutely. You can configure which Stack exchange 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 Stack exchange data and credentials are handled as safely as possible.

Used by agents from

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Altera
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Entelligence
Rolai

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