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 LlamaIndex 3 tools — connect in under 2 minutes

Name: UniProt
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 3 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add UniProt as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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 llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to UniProt. "
            "You have 3 tools available."
        ),
    )

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

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.

LlamaIndex agents combine UniProt tool responses with indexed documents for comprehensive, grounded answers. Connect 3 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex 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 LlamaIndex via MCP

Follow these steps to integrate the UniProt MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

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

Why Use LlamaIndex with the UniProt MCP Server

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

Data-first architecture: LlamaIndex agents combine UniProt tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain UniProt tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query UniProt, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what UniProt tools were called, what data was returned, and how it influenced the final answer

UniProt + LlamaIndex Use Cases

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

Hybrid search: combine UniProt real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query UniProt to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying UniProt for fresh data

Analytical workflows: chain UniProt queries with LlamaIndex's data connectors to build multi-source analytical reports

UniProt MCP Tools for LlamaIndex (3)

These 3 tools become available when you connect UniProt to LlamaIndex 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 LlamaIndex

Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

UniProt + LlamaIndex FAQ

Common questions about integrating UniProt MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query UniProt tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.