How to Use the Leiga MCP in Google ADK

Q: Can I limit which tools my Google ADK agent can access on the Leiga MCP Server?

Yes, you use the toolnames filter in your McpToolset setup. This lets you expose listtasks while blocking write operations like createtask.

Q: Can I join Leiga project metadata with BigQuery tables in Google ADK?

Absolutely. Your agent can call getprojectdetails to fetch current milestones and query BigQuery to check if the team is on track.

Connect Gemini models to Leiga using Google ADK to analyze massive backlogs alongside your BigQuery warehouse.

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Connect Leiga MCP to Google ADK

Create your Vinkius account to connect Leiga 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 Leiga with Google ADK

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Run deep backlog audits with Google ADK

This MCP Server lets Gemini models use `list_tasks` to ingest thousands of open tickets in a single prompt. Because Google ADK supports million-token context windows, your agent reads entire project histories to find hidden blockers. The agent compares this live agile data with your historical engineering metrics stored in BigQuery. It flags mismatched priorities without you having to export CSVs or write custom sync scripts.

Map enterprise team structures via Google ADK

This MCP Server connects Google ADK to your organization's structure using `list_organizational_teams`. Your agent queries this tool to match active developer squads against complex project requirements returned by `list_projects`. You can filter which tools are exposed using the ADK toolset parameters. If you only want the agent to read resource structures without changing them, simply omit `create_task` from the tool list.

Align active workflows with Google ADK agents

This MCP Server exposes your team's custom pipelines to Google ADK through the `list_workflows` tool. The Gemini model parses these rules to ensure new tickets created via `create_task` land in the correct initial state. The agent pulls status details using `get_task_details` to verify that no step was skipped. It keeps your project boards perfectly aligned with your actual deployment pipelines.

Setup guide

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

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Common questions about Leiga MCP in Google ADK

Yes, you use the `tool_names` filter in your `McpToolset` setup. This lets you expose `list_tasks` while blocking write operations like `create_task`.

The framework takes advantage of Gemini's large context window. It feeds the complete output of `list_tasks` directly into the model to analyze complex dependencies.

Absolutely. Your agent can call `get_project_details` to fetch current milestones and query BigQuery to check if the team is on track.

Yes, the ADK toolset handles both transport layers. For cloud-deployed agents, the HTTP streamable setup is the standard choice.

Your Leiga project metadata and custom workflow steps are never stored. Vinkius routes these requests through encrypted tunnels, ensuring your private team structures remain secure.

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