AutoGen MCP. Orchestrate entire multi-agent workflows from your agent client.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
AutoGen MCP Server lets you manage complex, multi-agent AI workflows. You can create isolated sessions, define custom agent roles (like Coder or Critic), and dispatch human prompts to trigger deep, agent-to-agent conversations.
It provides full visibility into the execution logs and allows your AI client to dynamically command and debug specialized agent swarms.
What your AI agents can do
Create agent
Define a new customized AutoGen agent using specific parameters.
Create message
Send a user message to start or continue an AutoGen session.
Create session
Create a new, blank AutoGen session memory space.
The create_session tool generates a clean, isolated memory boundary, guaranteeing that the current workflow's state won't leak into other concurrent tasks.
Use create_agent to dynamically map and customize specific LLM roles—like a dedicated Coder or a Critic—with defined parameters.
The create_message tool allows you to dispatch a human prompt to initiate or continue a multi-agent conversation within an existing session.
The list_messages tool retrieves the full, deep history of agent-to-agent conversations for a specific session ID.
Tools like list_agents and list_sessions let you audit the current set of configured agents and active conversation boundaries.
The list_skills tool lists all injected Python functions, allowing you to see what external, native code the agents can execute.
Ask AI about this MCP
Supported MCP Clients
Gemini Waiting for input…
019d7556create agent
Define a new customized AutoGen agent using specific parameters.
019d7556create message
Send a user message to start or continue an AutoGen session.
019d7556create session
Create a new, blank AutoGen session memory space.
019d7556delete session
Permanently remove an AutoGen session.
019d7556list agents
Retrieve a list of all configured AutoGen agents available.
019d7556list messages
Get the message history for a specific AutoGen session.
019d7556list models
List all Large Language Models configured for use in AutoGen.
019d7556list sessions
List all active AutoGen Studio conversation sessions.
019d7556list skills
List the Python skill functions available to AutoGen agents.
019d7556list workflows
List all predefined AutoGen multi-agent workflows.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with AutoGen, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
You're running complex agent workflows? This server lets your AI client manage multi-agent topologies and keep everything isolated. You'll get full control over the whole thing.
To start a new workflow, you'll use create_session to generate a clean, isolated memory space. This guarantees the state from one task won't leak into another concurrent job.
When you need a specific agent—say, a Coder or a Critic—you'll use create_agent to define and customize that LLM role with specific parameters. You're building a team, not just running a script. You can check out all the agents you've set up using list_agents, and see every active conversation boundary with list_sessions.
Ready to kick off the conversation? You'll use create_message to dispatch a human prompt, which kicks off or continues a multi-agent discussion within a specific session. Need to track what happened? list_messages pulls the full, deep history of agent-to-agent conversations for that session ID.
Need to see what else is running around? You can check the available LLMs with list_models, review all the custom Python functions the agents can execute using list_skills, or look at predefined multi-agent blueprints with list_workflows. You can also audit what agents you have with list_agents, or permanently wipe a session with delete_session.
It's simple: you set up the memory with create_session, define the specialized roles with create_agent, send the prompt with create_message, and then you're running a complex, controlled conversation.
How AutoGen MCP Works
- 1 First, run
create_sessionto establish a clean, isolated memory space for the task. - 2 Next, use
create_agentto define the specialized roles (e.g., Coder, Critic) that will participate in the workflow. - 3 Finally, send the initial prompt via
create_messageto trigger the agent group, which executes the defined workflow and returns the full conversation trace.
The bottom line is that your agent client can manage and debug specialized agent swarms by first setting up the environment, defining the participants, and then triggering the sequence.
Who Is AutoGen MCP For?
AI Engineers, Product Managers, and Researchers. If you're dealing with multi-step automation where the output of one AI module feeds into another, this is for you. You're tired of manually inspecting logs across several tabs just to figure out why the 'Coder' agent failed on step 4. This lets you treat the entire swarm as a single, debuggable process.
Uses list_agents and list_skills to audit running topologies and iterate on Python skills without needing to switch contexts or rewrite core logic.
Verifies the health and step-by-step actions of automated backend swarm operations, ensuring the complex workflow hits all necessary checkpoints.
Extracts deep LLM-to-LLM conversational histories across multiple experimental boundaries for grading and analysis.
What Changes When You Connect
- See the full conversation history. Use
list_messagesto pull deep traces of agent-to-agent interactions, so you know exactly who said what and why. - Define custom roles on the fly. The
create_agenttool lets you map specific LLM roles (like a Coder or Critic) using Python-based parameters, giving you granular control over the team structure. - Keep state clean. Use
create_sessionto generate a blank, isolated memory space. This guarantees that a failure in one workflow doesn't corrupt the state of another. - Audit the entire setup.
list_agentsandlist_workflowslet you view all configured agents and pre-defined multi-agent topologies before you run anything. - Control the execution path.
list_skillsshows every available native Python function, letting you confirm that the agents have access to the external code they need to run. - Debug complex failures. By combining
list_sessionsandlist_messages, you can track the exact flow of a complex, multi-stage task from start to finish.
Real-World Use Cases
Automating Code Review
A development team needs a simulated code review. They use create_session to start clean. They define agents using create_agent (one User_Proxy, one Coder, one Critic). They run the workflow by calling create_message with the initial diff. The Critic then rejects the solution, and the user can use list_messages to see the exact rejection reason and the Coder's subsequent attempts.
Market Research Simulation
A product team wants to test a new market hypothesis. They use list_workflows to find a 'Market Research' topology. They run the process by calling create_message into a new session. The agent group executes, and the user can later check the final conclusion using the history logs retrieved via list_messages.
Debugging Agent Logic
The initial run of a complex agent swarm fails. Instead of guessing, the engineer uses list_agents to check the roles and list_skills to verify the external Python functions. They then use list_messages to pinpoint the exact turn in the conversation where the logic broke.
Maintaining Multiple Projects
A company runs several independent AI tasks (e.g., Finance analysis, HR policy generation). They use list_sessions to see which work boundaries are active, and create_session to start a completely fresh, dedicated process for the next project.
The Tradeoffs
Treating agents like single prompts
Just calling create_message repeatedly without defining the agent roles or sessions first. This treats the complex system like a simple chat bot, losing the multi-agent context and making debugging impossible.
→
Always start by calling create_session to get a clean memory space. Then, define your specialized roles using create_agent before sending the first message with create_message. This establishes the necessary boundaries.
Assuming persistence
Running a workflow and assuming the state persists indefinitely, even after the connection closes. The session state is temporary and needs explicit management.
→
Use list_sessions to check which sessions are currently active. If you need to start a new, clean run, use create_session and remember to call delete_session when the task is complete to free up resources.
Ignoring external capabilities
Expecting the agent to solve a calculation or data parsing task without telling it how. The agent only knows what's programmed, not what's available in the environment.
→
First, call list_skills to see what native Python code is available. Then, ensure your agents are configured to use those capabilities when you run the workflow.
When It Fits, When It Doesn't
Use this server if your process requires multiple, distinct AI components (agents) to pass information back and forth to solve a single problem. If the task is a simple Q&A or data transformation that doesn't require 'discussion' or 'collaboration' between different AI roles, you probably don't need it. Don't use this if you only need to run a single, isolated LLM call; use a simple API wrapper for that. However, if you need the AI to act like a team—where one agent criticizes the output of another, or a coder builds a solution the user then reviews—this is the right tool. It gives you the full visibility into the internal logic, which is key.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Microsoft AutoGen. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Debugging complex agent failures is a nightmare of tabs and log files.
Today, if an automated workflow fails, you're dumped into a mess. You have to jump between the main execution dashboard, the agent-specific log tabs, and sometimes a separate message history viewer. You're clicking through timestamps, copying diffs, and trying to piece together who said what and why. It's slow, and it's impossible to get a single, clean view of the full process.
With the AutoGen MCP Server, you run the entire multi-agent process, and the history is centralized. You get a single source of truth for the conversation. You can use `list_messages` to retrieve the deep, chronological trace of every agent-to-agent exchange, making debugging immediate and reliable.
AutoGen MCP Server: Manage agent conversations and tasks
Before, setting up a specialized agent swarm meant manually configuring roles and ensuring the correct dependencies were loaded. This was a brittle, multi-step process that often broke when one small piece changed.
Now, your agent client handles the setup. You use `create_agent` to define the roles, `create_session` to isolate the work, and then `create_message` to kick off the entire specialized team. It’s a single, controlled sequence that ensures the full workflow runs correctly.
Common Questions About AutoGen MCP
How do I use the `create_session` tool with AutoGen MCP Server? +
You call create_session to get a unique, clean UUID. This UUID defines the isolated memory boundary for your specific task. After that, you use the UUID when creating agents or sending messages to ensure the workflow stays contained.
What is the difference between `list_agents` and `list_workflows`? +
list_agents shows the specific, defined LLM roles (like Coder or Critic) that can participate in a task. list_workflows shows the pre-built, multi-step topologies—the entire blueprint of how those agents interact.
Can I debug the conversation using `list_messages`? +
Yes. list_messages retrieves the complete, historical trace of every message sent between agents and the user for a given session ID. It's how you debug the 'why' behind the output.
Do I need to call `delete_session` after I'm done? +
Yes. Calling delete_session permanently removes the session's memory space. This is important for resource management and keeping your workspace clean.
How do I use `list_models` to check available LLM configurations? +
This tool lists all LLMs configured in your AutoGen Studio instance. You see the names and constraints (like gpt-4-turbo or llama-3) available for the agents to use.
What does `create_agent` require to define a new customized AutoGen agent? +
You need to provide specific parameters defining the agent's role, goals, and base capabilities. This allows you to customize how it interacts with other agents.
Can `list_skills` show me what Python functions agents can run? +
Yes, list_skills shows all the available Python skill functions. Agents use these functions to perform external actions, like making API calls or processing data.
Why should I use `list_sessions` instead of just starting a new task? +
Using list_sessions lets you see a full history of past conversations and active sessions. This helps you track multi-agent work that happened before your current task.
Can my AI agent debug a looping multi-agent conversation? +
Yes. You can instruct your primary agent to retrieve the message traces for a specific AutoGen session ID. It will instantly unpack the internal LLM-to-LLM conversation, highlighting exactly which secondary agent is looping, throwing errors, or deviating from the constraints without manual log parsing.
How do I add a new Python capability or skill dynamicly? +
Your agent can list currently mapped Python skills bound to the studio runtime. If you need a new capability, your primary AI can iterate on the script directly on your CLI/editor and once deployed in your studio, you can map it natively to customized agents via the creation parameters.
Can it trigger a Workflow to start executing a new complex task? +
Absolutely. Ask your agent to create a fresh, blank, and completely isolated session, then dispatch a newly constructed 'human message' targeting an existing Multi-Agent workflow topology. It initiates the whole automated logic sequence securely and remotely.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
More in this category
Paperspace
Provision and track powerful GPU workloads via Paperspace — list compute instances, fetch active deployments, trace team projects, and query Gradient environments via AI.
Google BigQuery
Empower your AI agent to query massive datasets via BigQuery — execute Standard SQL, track active jobs, and inspect table schemas natively.
Lindy (Autonomous AI Employees)
Manage autonomous AI employees via Lindy — trigger task runs, monitor reasoning logs, and audit app integrations.
You might also like
Cube.dev
Access your Cube semantic layer — execute queries, inspect generated SQL, manage pre-aggregations, and explore data metadata directly.
EBI Proteins API
Query the UniProt knowledge base for protein sequences, annotations, and functional data across millions of characterized entries.
Trimble
Equip your AI agent with enterprise-grade truck routing, commercial geocoding, and advanced toll cost logistics algorithms.