How to Use the Linear MCP in Google ADK

Q: How do I set up the Linear MCP Server with Google ADK?

Install the package with pip and initialize McpToolset with your Vinkius HTTP transport URL. Pass the toolset to your LlmAgent to instantly expose all eleven issue tracking tools to your Gemini model.

Q: Can Gemini analyze my entire Linear backlog using Google ADK?

Yes, Gemini's long-context window allows the agent to call listlinearissues repeatedly to digest your entire backlog. It can then categorize issues, identify trends, and suggest cleanups without running out of memory.

Q: How do I restrict Google ADK to specific Linear tools?

You can use the optional toolnames filter when instantiating your McpToolset in Python. This limits the agent to read-only tools like getlinearissue if you want to prevent accidental updates.

Connect Gemini's 1M token context window to Linear using the Google ADK to analyze entire issue backlogs.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Linear MCP to Google ADK

Create your Vinkius account to connect Linear to Google ADK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Linear with Google ADK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Analyze massive backlogs with long-context reasoning

This MCP Server relies on `list_linear_issues` and `list_linear_projects` to feed your entire project history into Gemini's massive context window. Instead of scanning tickets one by one, your agent reviews thousands of issues at once to find duplicate bug reports or highlight systemic engineering bottlenecks. Combining this server with the Google ADK lets you run deep-dive planning sessions. Your agent reads every open issue, maps dependencies, and drafts a release plan based on actual historical data.

Sync Google ADK alerts with Linear teams

The integration uses `list_linear_teams` and `list_linear_users` to map your cloud infrastructure alerts directly to real-world engineering teams. When an alert triggers in your Vertex AI pipeline, the agent finds the on-call engineer and assigns them the task. This setup bridges the gap between your cloud operations and your project tracker. You don't have to write custom glue code to route system errors to the right developers.

Update project status from BigQuery pipelines

This MCP toolset uses `update_linear_issue` and `check_linear_status` to close the loop between your data warehouse and your product backlog. If your BigQuery analytics show a feature has successfully rolled out to all users, the agent automatically updates the tracking ticket. You get automated project updates driven by actual product metrics. This removes manual status reporting and ensures your ticket board reflects what is actually running in production.

Setup guide

Set up Linear 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 Linear 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="Linear_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Linear 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 Linear. 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.

Connect Linear now

Real-time monitoring

Live

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.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Linear MCP in Google ADK

Install the package with pip and initialize `McpToolset` with your Vinkius HTTP transport URL. Pass the toolset to your `LlmAgent` to instantly expose all eleven issue tracking tools to your Gemini model.

Yes, Gemini's long-context window allows the agent to call `list_linear_issues` repeatedly to digest your entire backlog. It can then categorize issues, identify trends, and suggest cleanups without running out of memory.

You can use the optional `tool_names` filter when instantiating your `McpToolset` in Python. This limits the agent to read-only tools like `get_linear_issue` if you want to prevent accidental updates.

Yes, the Google ADK supports both transport methods. For managed cloud environments, the HTTP transport hosted on Vinkius is the easiest way to connect without managing local node processes.

The integration only accesses your issue text, comments, and team lists. All API calls route through an isolated, zero-trust V8 sandbox on Vinkius, ensuring your authentication tokens are encrypted and never exposed to the public internet.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript