How to Use the Range MCP in Google ADK

Q: Can my Google ADK agent write updates to Range?

Yes, your agent can use createupdate to post automated status reports based on cloud events. For instance, when a BigQuery job finishes or a Vertex AI pipeline completes, the agent can write a quick summary. This keeps the whole team informed without manual intervention.

Q: Can I limit which Range tools are exposed to the Google ADK?

You can use the optional toolnames filter when setting up your McpToolset to restrict exposure. This lets you hide write operations like createupdate while leaving read-only tools active. It is a simple way to enforce least-privilege security in enterprise environments.

Feed your Range team data into Gemini using the Google ADK to analyze months of updates in one go.

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Setup Range with Google ADK

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Feed status updates to Gemini using Google ADK

Using `list_updates` allows the Google ADK to fetch hundreds of past check-ins and feed them directly into Gemini's massive context window. This lets your enterprise agent spot long-term blockers that humans usually miss. Instead of manual reviews, the agent processes these updates alongside your BigQuery data to find productivity patterns. You get a clear picture of team velocity without wasting hours scrolling through old slack channels.

Sync company objectives with cloud databases

Calling `list_objectives` and `get_objective` connects your Range goals directly to your Google ADK agent. The agent pulls these objectives to verify if your cloud infrastructure projects are hitting their quarterly milestones. You can write Python scripts that cross-reference these goals with active Vertex AI pipelines. If a machine learning model deployment lags, your agent flags the corresponding objective automatically.

Retrieve granular team context instantly

Running `get_team` and `get_user` gives your Google ADK agent immediate access to your company's organizational chart. The agent uses these tools to identify who is responsible for specific tasks or projects. When a system alert triggers in Google Cloud, the agent queries the MCP Server to find the on-call engineer. This automates incident routing and cuts down the time it takes to resolve critical production bugs.

Setup guide

Set up Range 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 Range 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="Range_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Range tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Range MCP in Google ADK

You initialize McpToolset with the Vinkius HTTP URL and pass it to your LlmAgent constructor. The Google ADK automatically maps the 11 Range tools so Gemini can invoke them during reasoning loops. This setup takes less than ten lines of Python code.

Yes, your agent can use `create_update` to post automated status reports based on cloud events. For instance, when a BigQuery job finishes or a Vertex AI pipeline completes, the agent can write a quick summary. This keeps the whole team informed without manual intervention.

Gemini's long-context window easily handles the output from `list_updates` and `get_snippet` over several months. The Google ADK passes this raw text directly to the model, allowing you to ask complex questions about team progress using this MCP integration. You don't need to build custom vector databases or chunking strategies.

You can use the optional tool_names filter when setting up your McpToolset to restrict exposure. This lets you hide write operations like `create_update` while leaving read-only tools active. It is a simple way to enforce least-privilege security in enterprise environments.

Your team goals, updates, and user details are processed in zero-trust, ephemeral V8 sandboxes managed by Vinkius. No Range data is cached or stored permanently on the hosting infrastructure. The Google ADK communicates over secure, encrypted MCP Server channels to keep your corporate directory private.

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