How to Use the Groq MCP in Google ADK

Q: How do I instantiate the Groq MCP Server in my Google ADK agent?

Use the McpToolset class with your Vinkius HTTP transport URL. Pass this toolset directly into your LlmAgent constructor to expose the 10 high-speed inference tools to your Gemini models.

Q: Can I restrict which Groq tools my Google ADK agent can access?

Yes, you can. Use the optional toolnames filter in your McpToolset configuration to expose only specific tools like createchatcompletion while hiding the rest.

Feed real-time LPU inference results into your Google ADK enterprise workflows and BigQuery datasets without GPU latency.

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Connect Groq MCP to Google ADK

Create your Vinkius account to connect Groq 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 Groq with Google ADK

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Fast Google ADK BigQuery Context Injection

`create_chat_completion` generates immediate summaries of massive BigQuery datasets queried by your Google ADK agent. Instead of waiting on slow API roundtrips, the LPU hardware processes the retrieved tabular data in milliseconds. This MCP Server allows your Gemini-driven agents to maintain momentum during complex reasoning loops. You bridge the gap between your enterprise data warehouse and high-speed token generation.

Google ADK Long-Context Summarization

`summarize_text` condenses long documents that your Google ADK agent pulls from Vertex AI storage. While Gemini handles the ultra-long context reasoning, this tool offloads the fast extraction tasks to Groq's hardware. You configure the toolset via `McpToolset` to filter down to this specific summarizer. This keeps your agent's context window clean and avoids unnecessary processing overhead on Google Cloud.

Extract Clean Entities into Google ADK Pipelines

`extract_entities` identifies names, locations, and custom variables from unstructured text files before your Google ADK agent writes them to BigQuery. This tool runs instantly on Groq LPUs to keep your ingestion pipelines moving. By using the MCP Server, your agent strips out noise and formats the structured data automatically. Your downstream Google Cloud analytics tools receive clean, validated inputs without delay.

Setup guide

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

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Common questions about Groq MCP in Google ADK

Use the `McpToolset` class with your Vinkius HTTP transport URL. Pass this toolset directly into your `LlmAgent` constructor to expose the 10 high-speed inference tools to your Gemini models.

Yes, you can. Use the optional `tool_names` filter in your `McpToolset` configuration to expose only specific tools like `create_chat_completion` while hiding the rest.

While Vertex models excel at ultra-long reasoning, the LPU hardware delivers much faster raw token generation for task-specific tools like `fix_grammar` or `translate_text`.

Yes, the server supports both. For cloud-hosted Google ADK deployments, use the streamable HTTP transport on Vinkius to ensure reliable, serverless MCP Server connectivity.

Every database query and translation string passes through an isolated V8 sandbox that encrypts data in transit. The Vinkius runtime is entirely ephemeral, meaning no data persists on disk after your Google ADK session terminates.

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