How to Use the R2R MCP in Google ADK

Q: How do I initialize the R2R MCP Server in Google ADK?

Run pip install google-adk first. Then, define an McpToolset with your HTTP server parameters and pass it to the LlmAgent tools list.

Q: Can Gemini use R2R to search unstructured document collections with Google ADK?

Yes, Gemini uses the search tool to scan your R2R database. This allows the model to ground its answers in your verified enterprise files instead of making things up.

Q: Does the Google ADK support filtering tools from the R2R MCP Server?

Yes. You can use the optional tool names filter in the McpToolset constructor to limit the agent's access to specific tools like ragquery or gethealth.

Q: How does the agent list all available files?

The agent executes listdocuments to get a paginated list of all ingested files. This helps the agent verify if a specific document has been parsed and indexed.

Connect your Google ADK enterprise agents directly to R2R for long-context search and document retrieval.

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Setup R2R with Google ADK

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Connect Google ADK with R2R for Massive Context RAG

The `search` tool connects Gemini's million-token context window to your R2R vector database. Your agent can run vector search to pull targeted document chunks or execute `rag_query` to get synthesized answers from your files. The Google ADK toolset registers these tools automatically. Gemini uses its reasoning capabilities to decide when to search your documents versus when to rely on its internal training data.

Inspect and Manage Enterprise Collections

The `list_collections` tool organizes your knowledge bases, while `list_documents` verifies file statuses. Your agent can query these endpoints to understand the structure of your data before initiating a search. This is critical for enterprise pipelines where data is siloed across departments. By checking collections first, your agent avoids searching irrelevant folders and keeps search latency low.

Track System Status and Retrieve Specific Files

The `get_health` tool ensures your pipeline is active before running intensive queries. If the database is offline, your agent can gracefully degrade or alert your engineering team. When an agent needs details about a specific file, it calls `get_document` to fetch the metadata. This lets you build verification steps into your Vertex AI pipelines to ensure files are fully parsed.

Setup guide

Set up R2R 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 R2R 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="R2R_agent",
    model="gemini-2.0-flash",
    instruction="You have access to R2R tools via MCP.",
    tools=mcp_tools,
)

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Common questions about R2R MCP in Google ADK

Run `pip install google-adk` first. Then, define an `McpToolset` with your HTTP server parameters and pass it to the `LlmAgent` tools list.

Yes, Gemini uses the `search` tool to scan your R2R database. This allows the model to ground its answers in your verified enterprise files instead of making things up.

Yes. You can use the optional tool names filter in the `McpToolset` constructor to limit the agent's access to specific tools like `rag_query` or `get_health`.

The agent executes `list_documents` to get a paginated list of all ingested files. This helps the agent verify if a specific document has been parsed and indexed.

All document metadata and text snippets are processed within your isolated Google Cloud environment or private R2R instance. The MCP Server acts as a local proxy, passing data directly to Gemini without storing any of your files or vector indexes.

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