How to Use the Exa MCP in Google ADK

Q: Can I limit which Exa tools are exposed to my Google ADK agent?

Yes, use the toolnames filter when setting up your McpToolset. You can restrict the agent to only use the content extraction tool if you want to prevent arbitrary web searches.

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Connect Exa MCP to Google ADK

Create your Vinkius account to connect Exa 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 Exa with Google ADK

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Feed BigQuery insights to Google ADK

Your Google ADK agent pulls structured data from BigQuery and uses `exa_search` to find matching real-world updates on the web. This bridges the gap between your internal Google Cloud data warehouse and Exa's live web index. Because Gemini models handle massive context windows, you can ingest highly detailed Exa search results. The MCP Server ensures the returned web data format matches what your Google ADK pipeline expects.

Discover competitors with this MCP Server

Run `exa_find_similar` within your Google ADK workflow to find alternative sources based on URLs stored in Vertex AI. This allows your enterprise Google ADK agents to map out competitor groups using Exa's neural similarity search. The Google ADK toolset handles the Exa HTTP transport details under the hood. You get structured, semantic Exa matches that feed directly into your Vertex AI analytical models.

Deep context extraction for Gemini models

Use `exa_get_contents` to pull clean text from multiple URLs simultaneously into your Google ADK workflow. Google ADK agents can ingest these clean pages directly into Gemini's million-token context window for exhaustive reasoning. This avoids the overhead of writing custom scrapers in Google Cloud Functions to process Exa web targets. The Exa tool returns clean, parsed markdown that keeps your Gemini model's reasoning focused on facts.

Setup guide

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

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Common questions about Exa MCP in Google ADK

You initialize McpToolset with the server parameters and pass it to your LlmAgent. The agent can then call the search tools to query the web using neural embeddings.

Yes, use the tool_names filter when setting up your McpToolset. You can restrict the agent to only use the content extraction tool if you want to prevent arbitrary web searches.

The tool outputs structured JSON containing page text and highlights. Your Google ADK agent can pass this data directly to Vertex AI embedding models or store it in BigQuery.

It supports both transport types seamlessly. For cloud deployments, running the server over HTTP allows your Google Cloud Run instances to connect reliably.

All enterprise search parameters and target URL payloads are processed within an ephemeral V8 sandbox. This setup guarantees that your enterprise search parameters are never cached or exposed to external networks.

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