How to Use the PMC Open Access (PubMed Central) MCP in Google ADK

Q: How do I connect PMC Open Access (PubMed Central) to Google ADK?

Use McpToolset with StreamableHttpServerParameters pointing to your Vinkius URL. Pass this toolset to your LlmAgent to expose the medical search capabilities of the MCP Server.

Q: Can Gemini analyze full-text articles found by PMC Open Access (PubMed Central) via Google ADK?

Yes, the oadiscover tool retrieves the direct download URLs. Your Google ADK agent can fetch these files and load their contents into Gemini's massive context window for synthesis.

Q: How do I restrict the PMC Open Access (PubMed Central) MCP tools visible to Google ADK?

You pass a list of allowed tool names, like exportcitation and convertids, to the toolnames parameter when setting up your McpToolset instance.

Q: Does this support standard OAI-PMH harvesting?

Yes, the server exposes oailistidentifiers and oailistmetadataformats. Your agent can systematically crawl PubMed Central's repository using these standard protocols.

Feed PMC Open Access (PubMed Central) metadata directly into Gemini's million-token context using Google ADK.

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Connect PMC Open Access (PubMed Central) MCP to Google ADK

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Setup PMC Open Access (PubMed Central) with Google ADK

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Long-context research with Google ADK

The `oa_discover` tool finds downloadable XML and PDF paths from the PMC Open Access Subset, sending them straight to your Google ADK agent. Because Gemini supports over a million tokens of context, your agent can ingest dozens of full-text papers found by this tool in a single turn. You initialize the toolset using `McpToolset` with the Vinkius HTTP transport params. This lets your enterprise agent fetch literature metadata and immediately run deep-reasoning chains over entire medical journals.

Syncing PMC Open Access MCP Server data to BigQuery

The `oai_list_records` tool extracts large-scale metadata records from PubMed Central using the OAI-PMH protocol. This MCP tool lets your Google ADK agent coordinate this extraction, formatting the XML payloads so they can be loaded directly into BigQuery tables for SQL analysis. You can restrict your agent's scope by passing a specific list of verbs to `tool_names`. This ensures your agent only calls `oai_list_records` and `oai_get_record`, avoiding accidental calls to administrative endpoints.

Mapping medical IDs within Gemini pipelines

The `convert_ids` tool resolves discrepancies between PMCID, PMID, and DOI formats inside your Google ADK runtime. This resolves the common issue of mismatched academic records before you feed the data into Vertex AI embedding models. Your agent runs this conversion natively during execution. By keeping ID resolution inside the MCP toolset, you prevent model hallucinations when referencing complex medical studies.

Setup guide

Set up PMC Open Access (PubMed Central) 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 PMC Open Access (PubMed Central) 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="PMC Open Access (PubMed Central)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to PMC Open Access (PubMed Central) 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 PMC (PubMed Central). 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 PMC Open Access (PubMed Central) MCP in Google ADK

Use `McpToolset` with `StreamableHttpServerParameters` pointing to your Vinkius URL. Pass this toolset to your `LlmAgent` to expose the medical search capabilities of the MCP Server.

Yes, the `oa_discover` tool retrieves the direct download URLs. Your Google ADK agent can fetch these files and load their contents into Gemini's massive context window for synthesis.

You pass a list of allowed tool names, like `export_citation` and `convert_ids`, to the `tool_names` parameter when setting up your `McpToolset` instance.

Yes, the server exposes `oai_list_identifiers` and `oai_list_metadata_formats`. Your agent can systematically crawl PubMed Central's repository using these standard protocols.

All queries, including PMCIDs and search terms, are handled within ephemeral V8 isolates on Vinkius. No biomedical metadata or proprietary search parameters are stored permanently, keeping your research pipeline compliant with enterprise security standards.

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