How to integrate Context.dev MCP with Autogen

This guide walks you through connecting Context.dev to AutoGen using the Composio tool router. By the end, you'll have a working Context.dev agent that can extract pricing data from competitor pages, monitor brand mentions across target websites, scrape structured data from product pages through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Context.dev account through Composio's Context.dev MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Context.dev is a web data API platform for scraping, extraction, brand intelligence, monitoring, and structured web data. Use it to turn public web pages into clean, AI-ready data without building brittle scrapers.

11 Tools

Introduction

This guide walks you through connecting Context.dev to AutoGen using the Composio tool router. By the end, you'll have a working Context.dev agent that can extract pricing data from competitor pages, monitor brand mentions across target websites, scrape structured data from product pages through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a Context.dev account through Composio's Context.dev MCP server.

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

Also integrate Context.dev with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

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 Context.dev
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Context.dev tools
  • Run a live chat loop where you ask the agent to perform Context.dev 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 Context.dev MCP server, and what's possible with it?

The Context.dev MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Context.dev account. It provides structured and secure access to web content, brand intelligence, website design details, monitors, documents, and usage data, so your agent can scrape webpages, extract structured data, research brands, parse files, and review monitoring activity on your behalf.

  • Web scraping and structured extraction: Have your agent turn a public webpage into Markdown or crawl relevant website pages to extract information that matches your chosen structure and instructions.
  • Brand research and discovery: Let the agent search for companies by name or domain, then retrieve brand details such as logos, colors, industry, and descriptions.
  • Website design analysis: Direct your agent to inspect a website's colors, typography, spacing, shadows, fonts, and common component styles.
  • Document and file parsing: Instruct your agent to convert uploaded documents, images, source files, or data files into Markdown that an AI assistant can use.
  • Monitoring and usage oversight: Have your agent review web monitors, asynchronous scraping batches, WebDB collections, credit consumption, storage, row counts, and monitor limits.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK 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

Step by step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Context.dev account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
2

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.
3

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 Context.dev via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

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 Context.dev connections to use
5

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 Context.dev session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["context_dev"]
    )
    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 Context.dev tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

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
7

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 Context.dev assistant agent with MCP tools
    agent = AssistantAgent(
        name="context_dev_assistant",
        description="An AI assistant that helps with Context.dev 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 Context.dev tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Context.dev 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 Context.dev 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 Context.dev 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 Context.dev session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["context_dev"]
    )
    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 Context.dev assistant agent with MCP tools
        agent = AssistantAgent(
            name="context_dev_assistant",
            description="An AI assistant that helps with Context.dev 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 Context.dev 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 Context.dev 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 Context.dev, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

Every Context.dev action and event your agent gets out of the box.

Extract Structured Web Data

Crawl relevant pages from a website and return data matching a caller-provided JSON Schema and extraction instructions.

Get Brand

Retrieve enriched brand identity, logos, colors, industry, and description using exactly one domain, name, work email, ticker, direct URL, or transaction descriptor.

Get Monitor Limits

Return the connected account's plan and current monitor usage versus its monitor limit.

Get WebDB Usage

Return WebDB credit balance, period consumption, row count, storage, and per-collection usage for an optional time range.

Get Website Styleguide

Extract a website's colors, typography, spacing, shadows, font assets, and common component styles from either a domain or one direct URL.

List Batches

List and filter the connected account's asynchronous web batches from newest to oldest, returning one cursor-controlled page.

List Monitors

List and filter the connected organization's web monitors, returning one cursor-controlled page.

List WebDB Collections

List the connected account's WebDB collections and return one cursor-controlled page, including each collection's sources, extraction configuration, status, usage, and timestamps.

Parse File

Convert an uploaded document, image, source file, or data file into LLM-usable Markdown.

Scrape Webpage to Markdown

Render one public URL and return its main or CSS-selected content as Markdown.

Search Brands

Prefix-search Context.

FRAMEWORKS

How to build Context.dev MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Context.dev MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Context.dev MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Context.dev MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Context.dev MCP with Claude Cowork

Codex logo

Codex

Use Context.dev MCP with Codex

OpenClaw logo

OpenClaw

Use Context.dev MCP with OpenClaw

Hermes logo

Hermes

Use Context.dev MCP with Hermes

CLI logo

CLI

Use Context.dev MCP with CLI

Google ADK logo

Google ADK

Use Context.dev MCP with Google ADK

LangChain logo

LangChain

Use Context.dev MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Context.dev MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Context.dev MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Context.dev MCP with LlamaIndex

CrewAI logo

CrewAI

Use Context.dev MCP with CrewAI

FAQ

Frequently asked questions

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

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 Context.dev tools.

Yes, absolutely. You can configure which Context.dev 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.

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 Context.dev data and credentials are handled as safely as possible.

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