How to Use the EBI InterPro MCP in Pydantic AI

Q: How do I configure the EBI InterPro MCP Server using Pydantic AI?

Install the package using pip install "pydantic-ai-slimmcp" and initialize MCPToolset with your Vinkius HTTP endpoint. Pass the toolset instance into your Agent constructor to give your model type-safe access to the tools.

Q: Does Pydantic AI validate EBI InterPro search results?

Yes, when your agent calls searchentries, the framework validates the array of matches against internal Pydantic models. This ensures you get clean, typed lists of domain names and protein counts.

Q: Can I use Pydantic AI to parse PDB structures from EBI InterPro?

Yes, you call getentrystructures to retrieve physical structures associated with a family. Pydantic AI parses the PDB IDs and resolutions into typed fields, ensuring your agent never passes malformed structural coordinates.

Q: What is the difference between getprotein and getproteinentries in a Pydantic AI agent?

The getprotein tool returns overall metadata like length and source organism for a single chain. The getproteinentries tool specifically lists every annotated InterPro domain mapped to that protein, which is the key for functional profiling.

Validate EBI InterPro protein classifications at runtime with Pydantic AI type safety.

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Connect EBI InterPro MCP to Pydantic AI

Create your Vinkius account to connect EBI InterPro to Pydantic AI 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 InterPro with Pydantic AI

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Enforce strict schemas on protein domain data

The `get_protein` tool retrieves detailed protein metadata including names, lengths, and associated domain counters for your Pydantic AI pipeline. Pydantic AI validates this incoming EBI InterPro payload against strict runtime schemas, catching missing fields or unexpected data formats instantly. If the EBI database returns complex nested structures, the Pydantic AI framework parses them into typed Python models. This prevents silent corruption in your biological data pipelines, failing loudly if an EBI InterPro sequence record violates your defined data constraints.

Run type-safe family lookups with this MCP Server

The `get_entry` tool fetches InterPro metadata, literature counts, and associated Gene Ontology terms for Pydantic AI agents. Your Pydantic AI agent calls this tool using the unified `MCPToolset` class, ensuring every returned GO term matches your typed definitions. For database-wide validation, the Pydantic AI agent uses `list_entry_databases` to verify active source counts. Because the Pydantic AI framework is model-agnostic, you can run these type-safe validation loops using local models or commercial APIs.

Parse conserved domains without silent validation failures

The `get_cdd_entry` tool pulls curated alignment and structural models from the Conserved Domain Database directly into Pydantic AI. Pydantic AI validates these CDD accessions at the boundary, ensuring your agent only processes correctly structured domain models through this MCP connection. When verifying specific protein domains, the Pydantic AI agent triggers `get_pfam_entry` to extract Pfam-specific alignments. Any invalid accession format triggers an immediate validation error, protecting downstream Pydantic AI analysis from malformed EBI InterPro database inputs.

Setup guide

Set up EBI InterPro MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "ebi-interpro-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to EBI InterPro tools.",
)

result = await agent.run("List recent EBI InterPro transactions")
print(result.output)

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Common questions about EBI InterPro MCP in Pydantic AI

Install the package using `pip install "pydantic-ai-slim[mcp]"` and initialize `MCPToolset` with your Vinkius HTTP endpoint. Pass the toolset instance into your `Agent` constructor to give your model type-safe access to the tools.

Yes, when your agent calls `search_entries`, the framework validates the array of matches against internal Pydantic models. This ensures you get clean, typed lists of domain names and protein counts.

Yes, you call `get_entry_structures` to retrieve physical structures associated with a family. Pydantic AI parses the PDB IDs and resolutions into typed fields, ensuring your agent never passes malformed structural coordinates.

The `get_protein` tool returns overall metadata like length and source organism for a single chain. The `get_protein_entries` tool specifically lists every annotated InterPro domain mapped to that protein, which is the key for functional profiling.

Your accession numbers and query parameters are processed in memory within secure Vinkius V8 MCP isolates. No biological data is logged or stored on persistent disks, keeping your sequence queries private and isolated from external networks.

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