How to Use the LinearB MCP in Google ADK

Feed pipeline telemetry into Google ADK agents to analyze DORA metrics using this MCP Server.

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Connect LinearB MCP to Google ADK

Create your Vinkius account to connect LinearB 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.

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Setup LinearB with Google ADK

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Analyze pipeline health with Gemini models

The `query_software_metrics` tool pulls raw engineering metrics directly into your Gemini long-context window. This tool works alongside `list_software_deployments` to let your agent correlate delivery speed with your actual deploy volume over massive timeframes. By loading this MCP Server into the Google ADK, your agent can process millions of tokens of deployment history at once. You can cross-reference this telemetry with system logs stored in BigQuery to find the root cause of systemic delivery delays.

Map repository structures to Google ADK agents

The `list_connected_repos` tool retrieves all active repositories monitored by your delivery platform. Your agent uses this list to map codebases to specific development squads returned by `list_engineering_teams`. Integrating these tools with the Google ADK allows your Gemini-powered agent to build a live map of team code ownership. The agent can write these mappings directly to your cloud data warehouse for custom engineering dashboards.

Automate incident logging from Google Cloud

The `record_new_incident` tool registers production failures in your tracking dashboard as soon as they happen. This tool pairs with `list_software_incidents` so your agent can verify if an active outage has already been logged. Connecting this capability to your Google ADK agent lets you trigger automated incident reporting directly from Google Cloud monitoring webhooks. The agent parses the alert payload, matches it to a repository, and logs the incident without human intervention.

Setup guide

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

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Common questions about LinearB MCP in Google ADK

Install the Google ADK package and initialize the toolset with your HTTP server parameters. Pass this toolset directly to your LLM agent constructor to expose repository and deployment tools to Gemini.

Yes, your agent can run metrics queries and join that data with operational logs in BigQuery. Because the ADK integrates with Google Cloud services, you can write agents that automate this entire analysis.

Gemini can ingest months of deployment history in a single prompt. This allows your agent to spot subtle, long-term delivery trends and process massive metric payloads without hitting token limits.

Yes, you can use the tool names filter parameter when initializing your toolset. This lets you restrict your agent to read-only tools and block write operations like registering new deployments.

Your team structures and engineering metrics are processed within your secure cloud project boundary. The server runs in an isolated sandbox, ensuring your authentication tokens and pipeline data are never exposed.

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