How to Use the Greptile MCP in Google ADK

Connect Greptile to Google ADK to bring codebase-wide reasoning into your Gemini-powered enterprise infrastructure.

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Connect Greptile MCP to Google ADK

Create your Vinkius account to connect Greptile 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 Greptile with Google ADK

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Gemini-Ready Codebase Queries via Google ADK

Execute `query_codebase` and `query_with_context` to feed your Gemini models high-fidelity code data. This works natively within your Google ADK agent framework. Your agents can now reason over massive repositories as easily as they query BigQuery. It allows for a unified approach to data where code is treated as a first-class enterprise asset.

Manage Repositories with Google ADK MCP Server

Use `index_repository` and `delete_repository` to maintain your indexed codebase state. The Google ADK framework handles these tools through the standard MCP interface. This gives your automation the ability to clean up or update its search space on the fly. You aren't just reading code; you are managing the entire index lifecycle.

Search Codebases Efficiently in Google ADK

Run `search_codebase` to locate specific implementations across your entire project. The tool returns structured results that your agents can process immediately. Searching is no longer a manual task for your developers. Your agents do the legwork, returning precise file matches and contextual snippets directly into your pipeline.

Setup guide

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

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Common questions about Greptile MCP in Google ADK

The server connects via standard transport and exposes search tools to the LlmAgent. You can filter which tools are available to specific agents.

Yes. The server is designed to handle indexing for large codebases. Gemini models in your ADK setup can then reason over the context provided by these tools.

They are independent systems. You can use Greptile tools to fetch code context and use your existing Google ADK connections to query BigQuery in the same agent turn.

Yes. You can choose the transport that fits your deployment architecture. Most enterprise setups prefer the HTTP transport for better isolation.

Data access is strictly limited to the files you index. Your source code is stored on the Greptile backend, and only the specific fragments returned by tool calls are sent to your agent's memory.

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