How to Use the Europe PMC MCP in Google ADK

Q: How do I configure Europe PMC for Google ADK?

Initialize a McpToolset with your server URL using StreamableHttpServerParameters. Then pass that toolset to your LlmAgent constructor.

Feed Europe PMC literature directly into your Google ADK pipelines. Let Gemini agents analyze millions of grants alongside your BigQuery data.

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Connect Europe PMC MCP to Google ADK

Create your Vinkius account to connect Europe PMC 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 Europe PMC with Google ADK

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Deep Literature Mining with Google ADK

The `search_articles` tool drops 33 million life science publications straight into your Gemini agent's context window. It pulls full abstracts, DOIs, and journal metadata. You skip the manual API integration entirely. Gemini models can read dozens of papers at once. Your agent pulls the literature, cross-references it with internal clinical trial data in BigQuery, and synthesizes the findings. The MCP standard makes the connection invisible.

Analyze Funding Data

The `search_grants` tool queries the Grist API via your MCP Server to expose who funded what. Your agent retrieves grant numbers, investigator names, and institutional affiliations. It maps the financial backing of scientific discoveries. You can write the output directly to Vertex AI datasets. Enterprise users rely on this to track competitor funding or find potential research partners. The data arrives clean and ready for analysis.

Smart Query Construction

The `get_article_fields` tool tells your agent exactly how to search the database. It lists the valid fields for authors, dates, and publication types. The agent reads the rules before it writes the query. You can restrict which endpoints the agent sees using the `tool_names` filter in your setup. If you only want it looking up fields, you block the rest. It keeps your Google Cloud architecture tight and predictable.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Europe PMC. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Europe PMC MCP in Google ADK

Initialize a `McpToolset` with your server URL using `StreamableHttpServerParameters`. Then pass that toolset to your `LlmAgent` constructor.

They can read abstracts and metadata using the search tools. For full text, the agent uses the returned DOIs to fetch open-access PDFs if available.

Yes. Your agent fetches the publication records via the MCP server and can immediately write that structured JSON into BigQuery tables.

You can pass a specific list of names to the toolset configuration. This prevents the agent from calling grant searches if you only want literature.

Your proprietary Vertex AI data never leaves your environment. The server strictly retrieves external life science publications and Grist funding records. Vinkius runs the transport layer through a zero-trust, stateless sandbox.

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