# How to integrate Async interview MCP with Autogen ```json { "title": "How to integrate Async interview MCP with Autogen", "toolkit": "Async interview", "toolkit_slug": "async_interview", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/async_interview/framework/autogen", "markdown_url": "https://composio.dev/toolkits/async_interview/framework/autogen.md", "updated_at": "2026-05-12T10:02:07.772Z" } ``` ## Introduction This guide walks you through connecting Async interview to AutoGen using the Composio tool router. By the end, you'll have a working Async interview agent that can list all open interview jobs this week, update job title for the sales position, delete completed interview job for designer role through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Async interview account through Composio's Async interview MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Async interview with - [OpenAI Agents SDK](https://composio.dev/toolkits/async_interview/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/async_interview/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/async_interview/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/async_interview/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/async_interview/framework/codex) - [OpenClaw](https://composio.dev/toolkits/async_interview/framework/openclaw) - [Hermes](https://composio.dev/toolkits/async_interview/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/async_interview/framework/cli) - [Google ADK](https://composio.dev/toolkits/async_interview/framework/google-adk) - [LangChain](https://composio.dev/toolkits/async_interview/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/async_interview/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/async_interview/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/async_interview/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/async_interview/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 Async interview - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Async interview tools - Run a live chat loop where you ask the agent to perform Async interview 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 Async interview MCP server, and what's possible with it? The Async interview MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Async interview account. It provides structured and secure access to your interview management dashboard, so your agent can list jobs, update interview details, and delete completed jobs automatically on your behalf. - Comprehensive job listing and retrieval: Instantly fetch a complete list of all your interview jobs, making it easy to monitor hiring activity or review open positions. - Automated job updates: Direct your agent to update the details of any existing interview job—adjusting job info, interview deadlines, or requirements as your needs evolve. - Effortless job deletion: Have your agent permanently remove interview jobs after you've reviewed candidate responses, keeping your hiring dashboard organized and clutter-free. - Streamlined interview process management: Let your agent help you keep interview workflows up to date by making changes or cleaning up jobs without manual effort. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `ASYNC_INTERVIEW_DELETE_JOB` | Delete Job | Tool to delete an interview job. Use when you need to permanently remove a job after reviewing responses. | | `ASYNC_INTERVIEW_LIST_INTERVIEW_RESPONSES` | List Interview Responses | Tool to retrieve all interview responses with candidate details. Use when monitoring incoming responses or triggering follow-up workflows. | | `ASYNC_INTERVIEW_LIST_JOBS` | List Jobs | Tool to retrieve a list of all interview jobs. Use when you need to display or manage existing jobs. | | `ASYNC_INTERVIEW_UPDATE_JOB` | Update Job | Tool to update an existing interview job. Use when you need to modify job details after creation. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Async interview MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Async interview. Instead of manually wiring Async interview 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 Async interview 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 Async interview 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 Async interview 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 Async interview 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 Async interview session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["async_interview"] ) 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 Async interview 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 Async interview assistant agent with MCP tools agent = AssistantAgent( name="async_interview_assistant", description="An AI assistant that helps with Async interview 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 Async interview 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 Async interview 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 Async interview session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["async_interview"] ) 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 Async interview assistant agent with MCP tools agent = AssistantAgent( name="async_interview_assistant", description="An AI assistant that helps with Async interview 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 Async interview 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 Async interview 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 Async interview, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Async interview MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/async_interview/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/async_interview/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/async_interview/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/async_interview/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/async_interview/framework/codex) - [OpenClaw](https://composio.dev/toolkits/async_interview/framework/openclaw) - [Hermes](https://composio.dev/toolkits/async_interview/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/async_interview/framework/cli) - [Google ADK](https://composio.dev/toolkits/async_interview/framework/google-adk) - [LangChain](https://composio.dev/toolkits/async_interview/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/async_interview/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/async_interview/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/async_interview/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/async_interview/framework/crew-ai) ## Related Toolkits - [Ashby](https://composio.dev/toolkits/ashby) - Ashby is an applicant tracking system that handles job postings, candidate management, and hiring analytics. - [Bamboohr](https://composio.dev/toolkits/bamboohr) - BambooHR is a cloud-based HR management platform for small and mid-sized businesses. It streamlines employee data, HR workflows, and reporting in one easy interface. - [Breathe HR](https://composio.dev/toolkits/breathehr) - Breathe HR is cloud-based HR software for SMEs to manage employee data, absences, and performance. It simplifies HR admin, making it easy to keep employee records accurate and up to date. - [Connecteam](https://composio.dev/toolkits/connecteam) - Connecteam is a workforce management platform for deskless teams, streamlining operations, HR, and team communication. It helps businesses save time by automating scheduling, time tracking, and staff engagement tasks. - [Lever](https://composio.dev/toolkits/lever) - Lever is an applicant tracking system that blends sourcing, CRM, and analytics for recruiting. It helps companies scale hiring with collaborative workflows and actionable insights. - [Recruitee](https://composio.dev/toolkits/recruitee) - Recruitee is collaborative hiring software that centralizes recruitment tasks for teams. It streamlines sourcing, interviewing, and hiring so you can fill roles faster. - [Remote retrieval](https://composio.dev/toolkits/remote_retrieval) - Remote retrieval is a logistics automation tool for managing laptop and monitor returns. It streamlines return tracking, saving time and hassle for IT and ops teams. - [Sap successfactors](https://composio.dev/toolkits/sap_successfactors) - Sap successfactors is a cloud-based human capital management suite for HR, payroll, recruiting, and talent management. It helps organizations centralize employee data and streamline the entire employee lifecycle. - [Talenthr](https://composio.dev/toolkits/talenthr) - TalentHR is an intuitive, all-in-one HR tool for managing employee records, leave, and HR workflows. It streamlines HR operations so businesses can focus on people, not paperwork. - [Workable](https://composio.dev/toolkits/workable) - Workable is an all-in-one HR software platform that streamlines hiring, employee management, and payroll. It helps teams simplify recruiting, onboarding, and staff operations in one place. - [Workday](https://composio.dev/toolkits/workday) - Workday is a cloud-based ERP platform for HR, finance, and workforce analytics. It streamlines employee management, payroll, and business operations in a single system. - [Gmail](https://composio.dev/toolkits/gmail) - Gmail is Google's email service with powerful spam protection, search, and G Suite integration. It keeps your inbox organized and makes communication fast and reliable. - [Google Calendar](https://composio.dev/toolkits/googlecalendar) - Google Calendar is a time management service for scheduling meetings, events, and reminders. It streamlines personal and team organization with integrated notifications and sharing options. - [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices. - [Outlook](https://composio.dev/toolkits/outlook) - Outlook is Microsoft's email and calendaring platform for unified communications and scheduling. It helps users stay organized with powerful email, contacts, and calendar management. - [Twitter](https://composio.dev/toolkits/twitter) - Twitter is a social media platform for sharing real-time updates, conversations, and news. Stay connected, informed, and engaged with communities worldwide. - [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly. - [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure. - [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed. - [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Async interview MCP? With a standalone Async interview MCP server, the agents and LLMs can only access a fixed set of Async interview tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Async interview 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 Async interview tools. ### Can I manage the permissions and scopes for Async interview while using Tool Router? Yes, absolutely. You can configure which Async interview 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 Async interview data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)