How to Use the Rocket.Chat MCP in Google ADK

Q: Can I use Google ADK to filter which tools are visible in Rocket.Chat?

Yes, you can use the optional toolnames filter in your toolset configuration. This restricts the available functions to exactly what your agent needs to perform.

Bridge your Google ADK agents to Rocket.Chat to coordinate tasks and report findings across your enterprise stack.

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Connect Rocket.Chat MCP to Google ADK

Create your Vinkius account to connect Rocket.Chat to Google ADK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Rocket.Chat with Google ADK

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Claude

Perplexity Vinkius runs on Gemini

Gemini

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Sync chat with Google ADK

Push automated reports from your cloud infrastructure into active chat rooms. Use `chat_post_message` to broadcast critical updates to your team instantly. Your agents act as a bridge between your data and your human team. It keeps everyone informed by posting directly into the channels where your engineers already work.

Retrieve workspace intel

Gather channel and user details to inform your agent's decision logic. Use `get_channel_info` to check current room status before your agent decides where to post its findings. Knowing the state of the workspace prevents your agent from spamming inactive users. It ensures that every action is grounded in the current reality of your team's communication habits.

Organize active conversations

List existing rooms to identify where your agent should focus its attention. You can use `list_public_channels` or `list_direct_messages` to map the workspace environment. Your agent uses this list to route information to the right people. It turns raw data into actionable updates that your team sees immediately, without manual intervention.

Setup guide

Set up Rocket.Chat MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Rocket.Chat tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Rocket.Chat_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Rocket.Chat tools via MCP.",
    tools=mcp_tools,
)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Rocket.Chat. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

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visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

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One

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Common questions about Rocket.Chat MCP in Google ADK

Yes, you can use the optional tool_names filter in your toolset configuration. This restricts the available functions to exactly what your agent needs to perform.

The server handles message payloads efficiently, allowing your agent to process large amounts of information. You can summarize complex reports and post them into the chat without hitting rate limits.

The connection uses the standard MCP transport, which maintains a stable link to the server. Your agent can perform long-running tasks and report back to the workspace as needed.

All data stays within the request-response cycle between your agent and the server. No information is stored by the MCP server itself, keeping your private channel details confidential.

It does, provided your authentication token includes access to those private groups. You can verify your reach by calling `list_private_groups` to see what your agent can access.

Use it with your favorite AI tools

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