What is the difference between Swiss-Prot and TrEMBL entries?

Swiss-Prot contains 570K+ entries that have been manually reviewed and curated by expert biologists — the gold standard for protein annotation. TrEMBL contains 250M+ entries that are computationally annotated from gene sequences. Swiss-Prot entries are marked as 'reviewed' and are highly reliable; TrEMBL entries are automatically generated and may contain errors.

Do I need to register or pay for an API key?

No. UniProt REST API is completely free and open without any authentication. There are no rate limits for reasonable usage patterns. UniProt is funded by the National Institutes of Health (NIH), European Molecular Biology Laboratory (EMBL), and the Swiss Institute of Bioinformatics (SIB).

Can I retrieve full amino acid sequences for proteins?

Yes. Every protein entry includes the full amino acid sequence with length information. The sequence is returned in standard one-letter amino acid code. For very large proteins (10,000+ residues), the sequence may be truncated in the response but the full accession data is always provided for direct download.

UniProt MCP Server for Pydantic AI 3 tools — connect in under 2 minutes

Name: UniProt
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 3 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect UniProt through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to UniProt "
            "(3 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in UniProt?"
    )
    print(result.data)

asyncio.run(main())

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About UniProt MCP Server

Connect your AI agent to UniProt — the Universal Protein Resource — the world's definitive knowledge base for protein sequence and functional information.

Pydantic AI validates every UniProt tool response against typed schemas, catching data inconsistencies at build time. Connect 3 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Protein Search — Find proteins by name, gene symbol, function, or organism across 250M+ entries from both TrEMBL (auto-annotated) and Swiss-Prot (manually curated)
Accession Lookup — Get complete protein data by UniProt accession number (e.g., P04637 for human p53) including function, subcellular location, and full amino acid sequence
Gene Search — Find all proteins encoded by a specific gene name across multiple organisms to compare orthologs and paralogs

The UniProt MCP Server exposes 3 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect UniProt to Pydantic AI via MCP

Follow these steps to integrate the UniProt MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 3 tools from UniProt with type-safe schemas

Why Use Pydantic AI with the UniProt MCP Server

Pydantic AI provides unique advantages when paired with UniProt through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your UniProt integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your UniProt connection logic from agent behavior for testable, maintainable code

UniProt + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the UniProt MCP Server delivers measurable value.

Type-safe data pipelines: query UniProt with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple UniProt tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query UniProt and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock UniProt responses and write comprehensive agent tests

UniProt MCP Tools for Pydantic AI (3)

These 3 tools become available when you connect UniProt to Pydantic AI via MCP:

get_uniprot_protein

Get full protein details by UniProt accession ID

search_uniprot

Returns protein name, gene, organism, function, subcellular location, and sequence. Try: insulin, hemoglobin, p53, BRCA1, spike protein. Search UniProt for proteins by name, function, or keyword

search_uniprot_gene

Returns all protein isoforms and their functional annotations. Find proteins encoded by a specific gene

Example Prompts for UniProt in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with UniProt immediately.

"Tell me about the p53 tumor suppressor protein and its function."

"Find all proteins encoded by the BRCA1 gene."

"Look up UniProt accession Q9BYF1 and show me its full details."

Troubleshooting UniProt MCP Server with Pydantic AI

Common issues when connecting UniProt to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

UniProt + Pydantic AI FAQ

Common questions about integrating UniProt MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your UniProt MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.