Rocket.Chat MCP Server for AutoGen 10 tools — connect in under 2 minutes
Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Rocket.Chat as an MCP tool provider through Vinkius and every agent in the group can access live data and take action.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with McpWorkbench(
server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
transport="streamable_http",
) as workbench:
tools = await workbench.list_tools()
agent = AssistantAgent(
name="rocketchat_agent",
tools=tools,
system_message=(
"You help users with Rocket.Chat. "
"10 tools available."
),
)
print(f"Agent ready with {len(tools)} tools")
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Rocket.Chat MCP Server
Connect your conversational assistant directly to Rocket.Chat, the open-source team communication platform. This integration transforms your AI into an active participant capable of chatting, sending notifications to channels, identifying active users, and auditing chat room data organically within your workspace.
AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Rocket.Chat tools. Connect 10 tools through Vinkius and assign role-based access. a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.
What you can do
- Communicate Actively — Instruct your assistant to post messages into public channels or private direct messages (
chat_post_message,chat_send_message). Need to fix a typo? The AI can seamlessly edit (chat_update_message) or delete previous messages (chat_delete_message). - Explore Channels & Groups — Give your assistant vision over public discussions (
list_public_channels) or private channels you belong to (list_private_groups). You can then extract deep information about specific rooms usingget_channel_info. - Audit Users in the Network — Scan the entire user directory (
list_users) to locate team members and review their roles and connection status directly (get_user_info).
The Rocket.Chat MCP Server exposes 10 tools through the Vinkius. Connect it to AutoGen in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Rocket.Chat to AutoGen via MCP
Follow these steps to integrate the Rocket.Chat MCP Server with AutoGen.
Install AutoGen
Run pip install "autogen-ext[mcp]"
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Integrate into workflow
Use the agent in your AutoGen multi-agent orchestration
Explore tools
The workbench discovers 10 tools from Rocket.Chat automatically
Why Use AutoGen with the Rocket.Chat MCP Server
AutoGen provides unique advantages when paired with Rocket.Chat through the Model Context Protocol.
Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Rocket.Chat tools to solve complex tasks
Role-based architecture lets you assign Rocket.Chat tool access to specific agents. a data analyst queries while a reviewer validates
Human-in-the-loop support: agents can pause for human approval before executing sensitive Rocket.Chat tool calls
Code execution sandbox: AutoGen agents can write and run code that processes Rocket.Chat tool responses in an isolated environment
Rocket.Chat + AutoGen Use Cases
Practical scenarios where AutoGen combined with the Rocket.Chat MCP Server delivers measurable value.
Collaborative analysis: one agent queries Rocket.Chat while another validates results and a third generates the final report
Automated review pipelines: a researcher agent fetches data from Rocket.Chat, a critic agent evaluates quality, and a writer produces the output
Interactive planning: agents negotiate task allocation using Rocket.Chat data to make informed decisions about resource distribution
Code generation with live data: an AutoGen coder agent writes scripts that process Rocket.Chat responses in a sandboxed execution environment
Rocket.Chat MCP Tools for AutoGen (10)
These 10 tools become available when you connect Rocket.Chat to AutoGen via MCP:
chat_delete_message
You must provide both room ID and message ID. Deletes a message from a room
chat_post_message
Sends a message to a channel or user by name
chat_send_message
Sends a message to a specific room by ID
chat_update_message
Updates the text of an existing message
get_channel_info
Retrieves details for a specific channel
get_user_info
Retrieves detailed information for a specific user
list_direct_messages
Lists all active direct message rooms
list_private_groups
Lists all private groups (channels) the user is a member of
list_public_channels
Lists all public channels in the workspace
list_users
Lists all users in the workspace directory
Example Prompts for Rocket.Chat in AutoGen
Ready-to-use prompts you can give your AutoGen agent to start working with Rocket.Chat immediately.
"List all of my active direct messages."
"Send a welcome message to #general thanking the new members."
"Find and get the user info for the ID abCD123."
Troubleshooting Rocket.Chat MCP Server with AutoGen
Common issues when connecting Rocket.Chat to AutoGen through the Vinkius, and how to resolve them.
McpWorkbench not found
pip install "autogen-ext[mcp]"Rocket.Chat + AutoGen FAQ
Common questions about integrating Rocket.Chat MCP Server with AutoGen.
How does AutoGen connect to MCP servers?
Can different agents have different MCP tool access?
Does AutoGen support human approval for tool calls?
Connect Rocket.Chat with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Rocket.Chat to AutoGen
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.