How to Use the UniProt MCP in Pydantic AI

Q: What process does UniProt use with Pydantic AI for data retrieval?

You call getuniprotprotein and provide the accession ID. The MCP Server returns the data, which your agent then validates using Pydantic models to guarantee a clean output.

Q: Can Pydantic AI search UniProt for proteins by function?

Yes, searchuniprot handles this. You input the functional keyword or name, and the tool returns validated data points like organism and sequence information.

Q: How do I find gene isoforms using UniProt with Pydantic AI?

Use searchuniprotgene. Provide the specific gene name, and your agent receives a type-safe list of all associated protein isoforms and their functional annotations.

Q: Does the MCP Server support searching for varied protein types with Pydantic AI?

The searchuniprot tool supports querying by name, function, or keyword. The response guarantees that all returned fields—from gene to sequence—match your defined data schemas.

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Connect UniProt MCP to Pydantic AI

Create your Vinkius account to connect UniProt 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 UniProt with Pydantic AI

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Accessing Full Protein Records with Pydantic AI

Start by calling `get_uniprot_protein` and passing the accession ID. Because your agent uses Pydantic, you get a guaranteed, type-validated structure for all the protein details returned. This means if the API sends unexpected data, your agent fails loudly with an error—no silent corruption.

Searching UniProt by Keyword in Pydantic AI

The `search_uniprot` tool lets you search by name, function, or keyword. The response is automatically validated against your defined models, ensuring you get predictable data structures for the sequence and location. It’s about correctness first. You know exactly what fields to expect when querying UniProt.

Identifying Gene Isoforms via MCP Server

To map out gene relationships, use `search_uniprot_gene`. This tool finds all isoforms and annotations for a specific gene. Pydantic ensures that every field—like the annotation text or protein name—is correctly typed. This validation step is crucial when building reliable, mission-critical pipelines.

Setup guide

Set up UniProt 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": {
        "uniprot-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about UniProt MCP in Pydantic AI

You call `get_uniprot_protein` and provide the accession ID. The MCP Server returns the data, which your agent then validates using Pydantic models to guarantee a clean output.

Yes, `search_uniprot` handles this. You input the functional keyword or name, and the tool returns validated data points like organism and sequence information.

Use `search_uniprot_gene`. Provide the specific gene name, and your agent receives a type-safe list of all associated protein isoforms and their functional annotations.

The server handles highly structured biological datasets, specifically amino acid sequences and comprehensive functional annotation records from 250M+ proteins. This is core scientific research data.

The `search_uniprot` tool supports querying by name, function, or keyword. The response guarantees that all returned fields—from gene to sequence—match your defined data schemas.

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