How to Use the EBI Proteins API MCP in Google ADK

Q: How do I connect the EBI Proteins API MCP Server to Google ADK?

Use the McpToolset class with your Vinkius HTTP endpoint. Pass this toolset directly to your LlmAgent in Python so Gemini can start calling tools like getprotein and gettaxonomy.

Q: Can I restrict which EBI Proteins API tools are exposed to Google ADK?

Yes, you can use the optional toolnames filter in the ADK setup. This allows you to expose only specific tools like getmutagenesis while hiding the rest from your agent.

Q: How does Google ADK use the EBI Proteins API for mass-spec analysis?

Your agent calls getproteomics to check which peptides are experimentally detected. It then correlates this mass-spectrometry data with your BigQuery datasets using Gemini's reasoning capabilities.

Q: How does the agent handle coordinate mapping between proteins and genomes?

The agent calls getcoordinates to retrieve Ensembl gene, transcript, and translation IDs. It then maps these chromosome positions to your Google Cloud storage files for genomic analysis.

Connect your Google ADK agents to the EBI Proteins API MCP Server to analyze UniProt sequences inside Google Cloud.

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Connect EBI Proteins API MCP to Google ADK

Create your Vinkius account to connect EBI Proteins API 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 EBI Proteins API with Google ADK

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Query genetic variants directly inside Google ADK

`get_variation` extracts clinical significance, wild-type residues, and consequence types for your genomics pipeline. Your agent feeds this data directly into BigQuery tables for large-scale downstream analysis. By combining these live queries with Google ADK, you bypass local flatfile databases entirely. The agent cross-references live UniProt variation data with your enterprise datasets using Gemini's long-context window.

Validate antibody targets with this MCP Server

`get_antigen` retrieves experimentally validated protein targets from the Human Protein Atlas. Your agent uses this tool to check antibody binding regions against known sequence coordinates. Because Google ADK supports selective tool exposure, you can restrict your agent to only use antigen and coordinate tools like `get_coordinates`. This ensures the model stays focused on immunology tasks without distracting it with unrelated proteomic data.

Analyze entire proteomes using Google ADK

`search_proteomes` queries reference proteomes by organism name, returning detailed taxonomy and gene counts. The agent loads these massive lists directly into its 1M-token context window to perform global comparative genomics. You connect the MCP Server using the `McpToolset` class in Python. This lets Gemini models dynamically call `get_genecentric` to count related proteins across multiple proteomes in a single reasoning session.

Setup guide

Set up EBI Proteins API 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 EBI Proteins API 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="EBI Proteins API_agent",
    model="gemini-2.0-flash",
    instruction="You have access to EBI Proteins API tools via MCP.",
    tools=mcp_tools,
)

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Common questions about EBI Proteins API MCP in Google ADK

Use the `McpToolset` class with your Vinkius HTTP endpoint. Pass this toolset directly to your `LlmAgent` in Python so Gemini can start calling tools like `get_protein` and `get_taxonomy`.

Yes, you can use the optional `tool_names` filter in the ADK setup. This allows you to expose only specific tools like `get_mutagenesis` while hiding the rest from your agent.

Your agent calls `get_proteomics` to check which peptides are experimentally detected. It then correlates this mass-spectrometry data with your BigQuery datasets using Gemini's reasoning capabilities.

The agent calls `get_coordinates` to retrieve Ensembl gene, transcript, and translation IDs. It then maps these chromosome positions to your Google Cloud storage files for genomic analysis.

All mass-spectrometry data and residue-level PTM positions retrieved via `get_proteomics_ptm` are processed in ephemeral memory. No data is stored on Vinkius servers, ensuring your pipeline's biological queries remain confidential.

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