# How to integrate Specific MCP with Autogen

```json
{
  "title": "How to integrate Specific MCP with Autogen",
  "toolkit": "Specific",
  "toolkit_slug": "specific",
  "framework": "AutoGen",
  "framework_slug": "autogen",
  "url": "https://composio.dev/toolkits/specific/framework/autogen",
  "markdown_url": "https://composio.dev/toolkits/specific/framework/autogen.md",
  "updated_at": "2026-03-29T06:51:17.119Z"
}
```

## Introduction

This guide walks you through connecting Specific to AutoGen using the Composio tool router. By the end, you'll have a working Specific agent that can summarize top objections from last week’s calls, list sales calls needing follow-up actions, generate call analytics report for your team through natural language commands.
This guide will help you understand how to give your AutoGen agent real control over a Specific account through Composio's Specific MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Specific with

- [ChatGPT](https://composio.dev/toolkits/specific/framework/chatgpt)
- [Antigravity](https://composio.dev/toolkits/specific/framework/antigravity)
- [OpenAI Agents SDK](https://composio.dev/toolkits/specific/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/specific/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/specific/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/specific/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/specific/framework/codex)
- [Cursor](https://composio.dev/toolkits/specific/framework/cursor)
- [VS Code](https://composio.dev/toolkits/specific/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/specific/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/specific/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/specific/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/specific/framework/cli)
- [Google ADK](https://composio.dev/toolkits/specific/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/specific/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/specific/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/specific/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/specific/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/specific/framework/crew-ai)

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

The Specific MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Specific account. It provides structured and secure access so your agent can perform Specific operations on your behalf.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `SPECIFIC_CREATE_COMPANY` | Create Company | Tool to create a new company record in the Specific platform. Use when you need to add a new company to the workspace with optional ID, name, and custom attributes. |
| `SPECIFIC_CREATE_CONVERSATION` | Create Conversation | Tool to create a new conversation (survey response) in the Specific platform. Use when you need to add a new conversation with content, optional contacts, companies, and custom fields. Content can be a simple string or structured ProseMirror document with headings and paragraphs. |
| `SPECIFIC_CREATE_OR_UPDATE_COMPANY` | Create or Update Company | Tool to upsert a company record in the Specific platform (create if not exists, update if exists). Use when you need to ensure a company exists with specific data, creating or updating as necessary based on the filter criteria. |
| `SPECIFIC_CREATE_OR_UPDATE_USER` | Create Or Update User | Tool to upsert a user record in the Specific platform (creates if not exists, updates if exists). Use when you need to ensure a contact exists with specific data, merging with existing records based on email or ID. |
| `SPECIFIC_CREATE_USER` | Create User | Tool to create a new user (contact) record in the Specific platform. Use when you need to add a new contact with email, name, and optional custom attributes or company association. |
| `SPECIFIC_DELETE_COMPANY` | Delete Company | Tool to delete a company record from the Specific platform. Use when you need to remove a company by its ID. The operation is permanent and returns the deleted company's details. |
| `SPECIFIC_DELETE_COMPANY_ATTRIBUTES` | Delete Company Attributes | Tool to delete specific custom field attributes from a company record. Use when you need to remove custom attributes by their keys from a company. Returns the updated company details. |
| `SPECIFIC_DELETE_USER` | Delete User | Tool to remove a user record from the Specific platform. Use when you need to permanently delete a contact from the workspace. |
| `SPECIFIC_DELETE_USER_ATTRIBUTES` | Delete User Attributes | Tool to delete specific custom field attributes from a user record in the Specific platform. Use when you need to remove custom field values from a contact without deleting the contact itself. |
| `SPECIFIC_GET_MY_WORKSPACE` | Get My Workspace | Tool to get current workspace information for the authenticated user. Use when you need to retrieve the workspace ID and name for the authenticated API key. |
| `SPECIFIC_LIST_COMPANIES` | List Companies | Tool to query company records from the Specific platform via GraphQL. Use when you need to retrieve company information, optionally filtering by ID or name. |
| `SPECIFIC_LIST_CONVERSATIONS` | List Conversations | Tool to query conversation records (survey responses) from the Specific platform via GraphQL. Use when you need to retrieve customer feedback, survey responses, or conversation history. Returns up to 20 most recent conversations, optionally filtered by source identifiers. |
| `SPECIFIC_LIST_CUSTOM_FIELDS` | List Custom Fields | Tool to query custom field definitions in the Specific platform. Use when you need to retrieve available custom fields that can be associated with companies, contacts, or conversations. Optionally filter by entity type. |
| `SPECIFIC_LIST_SOURCES` | List Sources | Tool to retrieve all data sources from the Specific platform. Use when you need to fetch available sources in the workspace. |
| `SPECIFIC_LIST_SURVEYS` | List Surveys | Tool to retrieve multiple survey records from the Specific platform via GraphQL. Use when you need to fetch all available surveys with their metadata, including names, contexts, tones, and associated conversation data. |
| `SPECIFIC_LIST_USERS` | List Users | Tool to query user accounts from the Specific platform via GraphQL. Use when you need to retrieve user information, optionally filtering by ID, email, or custom attributes. |
| `SPECIFIC_UPDATE_COMPANY` | Update Company | Tool to update an existing company record in the Specific platform. Use when modifying company name or custom attributes. |
| `SPECIFIC_UPDATE_USER` | Update User | Tool to modify an existing user record in the Specific platform via GraphQL mutation. Use when you need to update contact information such as name, email, company association, or custom attributes. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Specific MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Specific. Instead of manually wiring Specific APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

You will need:
- A Composio API key
- An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
- A Specific account you can connect to Composio
- Some basic familiarity with Autogen and Python async

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

Install Composio, Autogen extensions, and dotenv.
What's happening:
- composio connects your agent to Specific via MCP
- autogen-agentchat provides the AssistantAgent class
- autogen-ext-openai provides the OpenAI model client
- autogen-ext-tools provides MCP workbench support
```bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools
```

### 3. Set up environment variables

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 Specific connections to use
```bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com
```

### 4. Import dependencies and create Tool Router session

What's happening:
- load_dotenv() reads your .env file
- Composio(api_key=...) initializes the SDK
- create(...) creates a Tool Router session that exposes Specific tools
- session.mcp.url is the MCP endpoint that Autogen will connect to
```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 Specific session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["specific"]
    )
    url = session.mcp.url
```

### 5. Configure MCP parameters for Autogen

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
```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")}
)
```

### 6. Create the model client and agent

What's happening:
- OpenAIChatCompletionClient wraps the OpenAI model for Autogen
- McpWorkbench connects the agent to the MCP tools
- AssistantAgent is configured with the Specific tools from the workbench
```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 Specific assistant agent with MCP tools
    agent = AssistantAgent(
        name="specific_assistant",
        description="An AI assistant that helps with Specific operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )
```

### 7. Run the interactive chat loop

What's happening:
- The script prompts you in a loop with You:
- Autogen passes your input to the model, which decides which Specific 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
```python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Specific 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")
```

## Complete Code

```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 Specific session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["specific"]
    )
    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 Specific assistant agent with MCP tools
        agent = AssistantAgent(
            name="specific_assistant",
            description="An AI assistant that helps with Specific 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 Specific 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 Specific 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 Specific, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

## How to build Specific MCP Agent with another framework

- [ChatGPT](https://composio.dev/toolkits/specific/framework/chatgpt)
- [Antigravity](https://composio.dev/toolkits/specific/framework/antigravity)
- [OpenAI Agents SDK](https://composio.dev/toolkits/specific/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/specific/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/specific/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/specific/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/specific/framework/codex)
- [Cursor](https://composio.dev/toolkits/specific/framework/cursor)
- [VS Code](https://composio.dev/toolkits/specific/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/specific/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/specific/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/specific/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/specific/framework/cli)
- [Google ADK](https://composio.dev/toolkits/specific/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/specific/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/specific/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/specific/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/specific/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/specific/framework/crew-ai)

## Related Toolkits

- [Aeroleads](https://composio.dev/toolkits/aeroleads) - Aeroleads is a B2B lead generation platform for finding business emails and phone numbers. Grow your sales pipeline faster with powerful prospecting tools.
- [Autobound](https://composio.dev/toolkits/autobound) - Autobound is an AI-powered sales engagement platform that crafts hyper-personalized outreach and insights. It helps sales teams boost response rates and close more deals through tailored content and recommendations.
- [Better proposals](https://composio.dev/toolkits/better_proposals) - Better Proposals is a web-based tool for crafting and sending professional proposals. It helps teams impress clients and close deals faster with slick, easy-to-use templates.
- [Bidsketch](https://composio.dev/toolkits/bidsketch) - Bidsketch is a proposal software that helps businesses create professional proposals quickly and efficiently. It streamlines the proposal process, saving time while boosting client win rates.
- [Bolna](https://composio.dev/toolkits/bolna) - Bolna is an AI platform for building conversational voice agents. It helps businesses automate support and streamline interactions through natural, voice-powered conversations.
- [Botsonic](https://composio.dev/toolkits/botsonic) - Botsonic is a no-code AI chatbot builder for easily creating and deploying chatbots to your website. It empowers businesses to offer conversational experiences without writing code.
- [Botstar](https://composio.dev/toolkits/botstar) - BotStar is a comprehensive chatbot platform for designing, developing, and training chatbots visually on Messenger and websites. It helps businesses automate conversations and customer interactions without coding.
- [Callerapi](https://composio.dev/toolkits/callerapi) - CallerAPI is a white-label caller identification platform for branded caller ID and fraud prevention. It helps businesses boost customer trust while stopping spam, fraud, and robocalls.
- [Callingly](https://composio.dev/toolkits/callingly) - Callingly is a lead response management platform that automates immediate call and text follow-ups with new leads. It helps sales teams boost response speed and close more deals by connecting seamlessly with CRMs and lead sources.
- [Callpage](https://composio.dev/toolkits/callpage) - Callpage is a lead capture platform that lets businesses instantly connect with website visitors via callback. It boosts lead generation and increases your sales conversion rates.
- [Clearout](https://composio.dev/toolkits/clearout) - Clearout is an AI-powered service for verifying, finding, and enriching email addresses. It boosts deliverability and helps you discover high-quality leads effortlessly.
- [Clientary](https://composio.dev/toolkits/clientary) - Clientary is a platform for managing clients, invoices, projects, proposals, and more. It streamlines client work and saves you serious admin time.
- [Convolo ai](https://composio.dev/toolkits/convolo_ai) - Convolo ai is an AI-powered communications platform for sales teams. It accelerates lead response and improves conversion rates by automating calls and integrating workflows.
- [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment.
- [Docsbot ai](https://composio.dev/toolkits/docsbot_ai) - Docsbot ai is a platform that lets you build custom AI chatbots trained on your documentation. It automates customer support and content generation, saving time and improving response quality.
- [Emelia](https://composio.dev/toolkits/emelia) - Emelia is an all-in-one B2B prospecting platform for cold-email, LinkedIn outreach, and prospect research. It streamlines outbound campaigns so you can find, engage, and warm up leads faster.
- [Findymail](https://composio.dev/toolkits/findymail) - Findymail is a B2B data provider offering verified email and phone contacts for sales prospecting. Enhance outreach with automated exports, email verification, and CRM enrichment.
- [Freshdesk](https://composio.dev/toolkits/freshdesk) - Freshdesk is customer support software with ticketing and automation tools. It helps teams streamline helpdesk operations for faster, better customer support.
- [Fullenrich](https://composio.dev/toolkits/fullenrich) - FullEnrich is a B2B contact enrichment platform that aggregates emails and phone numbers from 15+ data vendors. Instantly find and verify lead contact data to boost your outreach.
- [Gatherup](https://composio.dev/toolkits/gatherup) - GatherUp is a customer feedback and online review management platform. It helps businesses boost their reputation by streamlining how they collect and manage customer feedback.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Specific MCP?

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

### Can I manage the permissions and scopes for Specific while using Tool Router?

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

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)
