How to Use the UniProt MCP in LlamaIndex

Q: How do I improve my UniProt search using LlamaIndex?

Run a search with searchuniprot first. By indexing the results, you create a persistent knowledge base that lets your agent query and compare findings over time.

Q: Can I retrieve specific protein details about UniProt using LlamaIndex?

Yes. Indexing the output of getuniprotprotein allows you to reference precise, historical data points from past sessions, keeping your RAG application accurate.

Q: What kind of annotations are available for UniProt using LlamaIndex?

When searching by gene with searchuniprotgene, you index functional annotations and isoforms. This builds a robust knowledge layer on top of the core database.

Q: Does the MCP Server support keyword searching in LlamaIndex?

Yes, running searchuniprot allows your agent to use keywords (like 'spike protein') and immediately index those results for later semantic search within your knowledge base.

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Connect UniProt MCP to LlamaIndex

Create your Vinkius account to connect UniProt to LlamaIndex 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 LlamaIndex

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Perplexity

Search and index broad protein records.

The `search_uniprot` tool lets you query the database by name, function, or keyword. The key here is that the output becomes part of your searchable knowledge base. You can now ask follow-up questions about 'p53' without re-running the search. This process grounds your RAG application in factual API data, preventing hallucinations and letting you query past findings.

Retrieve and index specific protein details.

Use `get_uniprot_protein` to pull a complete record using an accession ID. When you pass this through LlamaIndex, the entire dataset—sequences, locations, functions—is indexed. Later, your agent can query this historical data set and retrieve specific facts about that protein without needing the original tool call.

Create a unified index of gene annotations.

The `search_uniprot_gene` function gathers all isoforms for a given gene. By indexing this output, you create a rich knowledge graph of protein variation. This lets your RAG application answer complex questions like: 'What are the known functions across all isoforms of BRCA1?'

Setup guide

Set up UniProt MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all UniProt MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to UniProt tools.",
)
response = await agent.run("List recent UniProt data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by UniProt. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about UniProt MCP in LlamaIndex

Run a search with `search_uniprot` first. By indexing the results, you create a persistent knowledge base that lets your agent query and compare findings over time.

Yes. Indexing the output of `get_uniprot_protein` allows you to reference precise, historical data points from past sessions, keeping your RAG application accurate.

When searching by gene with `search_uniprot_gene`, you index functional annotations and isoforms. This builds a robust knowledge layer on top of the core database.

Yes, running `search_uniprot` allows your agent to use keywords (like 'spike protein') and immediately index those results for later semantic search within your knowledge base.

This server touches public protein sequences and functional annotations. Since the MCP Server only provides biological research data, there's no private user information for your agent to worry about.

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