UniProt MCP for AI. Query Protein Sequences & Functional Annotations

Q: How do I use searchuniprot to find proteins by function?

You pass the functional keyword directly into searchuniprot. For example, if you want all enzymes that process lipids, you'd input 'lipid metabolizing enzyme'. The tool returns candidates and their sequences.

Q: What is the difference between searchuniprot and getuniprotprotein?

Use searchuniprot when you are guessing or researching, using a keyword like 'spike protein'. Use getuniprotprotein only if you have a precise UniProt accession ID (like P04637) and need the complete record.

Q: Can I find all proteins from a gene name with searchuniprotgene?

Yes. searchuniprotgene is designed for exactly that. Give it the gene symbol (e.g., 'BRCA1'), and it returns every known isoform across multiple species, which is crucial for evolutionary comparison.

Q: If I need to check multiple related genes, is it better to run searchuniprotgene or searchuniprot?

You should use searchuniprotgene. This tool specifically compiles all protein isoforms and their annotations for a single gene across different organisms, giving you a more complete comparison set.

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

Connect to your AI in seconds.

UniProt connects your AI agent directly to UniProt: the massive knowledge base for protein sequences. It lets you search 250M+ entries using keywords, find proteins related to a specific gene, or pull full functional details using an accession ID.

If you work with proteomics, this is where the data lives.

What your AI can do

Get uniprot protein

Gets the full protein details, including sequence and function, when you provide a specific UniProt accession ID.

Search uniprot gene

Finds all protein isoforms and functional annotations associated with a specific gene name across different species.

Search uniprot

Searches for proteins using general keywords like name or function. It returns basic data plus the amino acid sequence.

Search by Keyword or Function

Run a broad query using search_uniprot to find proteins associated with general terms like 'hemoglobin' or 'p53'.

Retrieve Full Protein Record

Use the accession ID in get_uniprot_protein to get every piece of functional and sequence data for a single protein.

Compare Gene Orthologs

Run search_uniprot_gene by entering a gene name; the agent returns all known isoforms encoded by that gene across different organisms.

Ask an AI about this

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AI Agent

UniProt MCP Server: 3 Tools for Protein Data Access

Access the world's largest protein knowledge base. These three tools let your AI agent search by keyword, find full records via ID, or compare gene isoforms.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using UniProt on Vinkius

Get Uniprot Protein

Gets the full protein details, including sequence and function, when you provide a specific UniProt accession ID.

Search Uniprot Gene

Finds all protein isoforms and functional annotations associated with a specific...

Search Uniprot

Searches for proteins using general keywords like name or function. It returns basic...

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Claude AI

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

Start a conversation

Open a new chat. The UniProt integration is available immediately — no restart needed.

Antigravity

Configure Agent Environment

Open your Antigravity agent's workspace configuration or mcp-servers.json file.

Bind the Endpoint

Add the Vinkius endpoint URL to your agent's MCP connections list:

"mcp_servers": {
  "uniprot": {
    "serverUrl": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
  }
}

Provide your secure token in place of [YOUR_TOKEN_HERE] to ensure your agent requests are authenticated.

Execute

Start your Antigravity session. The agent will autonomously discover and utilize the UniProt tools with full Vinkius guardrails applied.

VS Code Copilot

⚡

One-Click Install (Recommended)

In your Vinkius Dashboard, simply click the Add to VS Code button for this server. We'll automatically configure your local workspace.

Or configure manually

Open MCP Settings

Open VS Code, press Ctrl/Cmd + Shift + P, and search for GitHub Copilot: MCP Servers.

Add Server Config

Add the Vinkius endpoint configuration to your mcp-servers.json file:

"uniprot": {
  "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}

Ensure you replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

LangChain

Install Dependencies

Install the LangChain MCP adapters for your environment:

pip install langchain-mcp-adapters

Connect the Server

Use the SSEClient in LangChain to connect to the Vinkius managed endpoint:

from langchain_mcp_adapters.client import SSEClient

# Connect to Vinkius
client = SSEClient(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
tools = client.get_tools()

CrewAI

Define the Tool

Load the Vinkius MCP tools into your CrewAI agents:

from crewai import Agent
from mcp_crewai import MCPTool

# Connect securely to Vinkius
vinkius_tools = MCPTool(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

# Assign to Agent
researcher = Agent(
    role='Data Researcher',
    tools=vinkius_tools.get_all()
)

Execute Task

Run your CrewAI process. The agent will autonomously route tasks to the Vinkius managed server.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

Import from OpenAPI, Swagger, or YAML specs
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Publish to catalog or keep private

Start building

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Start with UniProt, then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.

Use this MCP plus 5,000+ others, all in one place
Add new capabilities to your AI anytime you want
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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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Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 3 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Finding protein data shouldn't require knowing three different API endpoints.

Today, getting a full picture of a target protein means jumping between pages. You might start with a general search to find the name, copy an ID from a list, and then use another tool just to pull the sequence. It's fragmentation—you spend more time on data retrieval mechanics than analysis.

With this MCP server, you feed your query once. Whether it's a broad keyword or a specific gene name, the agent handles the routing through `search_uniprot` or `search_uniprot_gene`. You get clean, structured JSON directly in your workspace.

UniProt MCP Server: Get complete protein records with `get_uniprot_protein`

The biggest time sink is the 'deep dive.' You find a promising hit, but you suspect there's more data—maybe its subcellular location or an obscure modification. Manually checking secondary resources adds hours to your project.

Now, if you have that UniProt accession number, running `get_uniprot_protein` instantly pulls the canonical record. It’s a single call for all the granular detail; no more hopping between sheets and databases.

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What your AI can actually do with this

UniProt hooks your AI agent right into UniProt—it’s the giant knowledge base for protein sequences. If you work in proteomics, this is where you gotta go to get the data. You can search over 250 million entries using keywords, pull out proteins linked to a specific gene, or grab full functional details just from an accession ID.

When your agent uses these tools, it doesn't just give you vague summaries; it runs precise queries against the massive database and spits back structured JSON. You get sequence data, function breakdowns, gene names, and subcellular locations—all ready to use.

If you're starting broad, you can run search_uniprot using general keywords for things like 'hemoglobin' or a specific disease pathway. This tool pulls up basic protein info (name, organism, function) along with the amino acid sequence so you know what you're looking at right off the bat.

Need to compare proteins across different species? You gotta use search_uniprot_gene. Just drop in a gene name, and the agent pulls all known isoforms and functional annotations for that gene across multiple organisms. That makes comparative analysis way easier.

When you've narrowed it down and you know exactly which protein you want, pull up its accession ID. Then you use get_uniprot_protein. This is your direct lookup: it grabs every piece of functional and sequence data for that single protein—its full record, function summaries, location details, and the complete amino acid sequence.

It's the deep dive you need without any guesswork.

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Here's how it actually works

The bottom line is that you send a specific biological query, and the agent gets back structured data from the massive UniProt database.

First, tell your AI client which protein data you need: do you have an ID (use get_uniprot_protein), a general term (use search_uniprot), or a gene name (use search_uniprot_gene)?

The agent executes the specific tool call, sending parameters like 'BRCA1' or 'P04637' to the server.

The UniProt MCP Server runs the query against the database and returns structured JSON containing all requested details: sequence, function, location, etc.

Who is this actually for?

Molecular biologists, bioinformaticians, and drug discovery researchers need this. If your job involves analyzing protein structure or function—or if you're constantly copy-pasting sequences to a spreadsheet—you need this server. It handles the raw data retrieval so you don't have to build complex API wrappers.

Bioinformatician

Uses search_uniprot and search_uniprot_gene to pull sequences for large-scale comparative analysis across dozens of organisms.

Molecular Biologist

Runs get_uniprot_protein when designing an experiment, needing the precise function and location details for a known target like p53.

Drug Discovery Scientist

Queries proteins by function (e.g., 'metalloprotease') using search_uniprot to identify potential therapeutic targets quickly.

What Changes When You Connect

Get full protein records instantly. Use get_uniprot_protein with the accession ID to pull every known function, location, and amino acid sequence for a single target.

Search by context, not just name. Running search_uniprot_gene on 'BRCA1' lets you compare all related isoforms across different organisms without multiple API calls.

Avoid manual database browsing. The search_uniprot tool handles broad queries using keywords (like 'spike protein'), giving you an immediate set of candidate proteins and their sequences.

Work with diverse data types. You get functional annotations, gene names, subcellular locations, and the raw amino acid sequence all in one structured output.

Minimize redundancy. Instead of running three different searches for a single target, use get_uniprot_protein to pull the complete, verified record.

See it in action

01 01

Comparing Orthologs

A bioinformatician needs to compare the structure of p53 across human and mouse. Instead of searching two databases manually, they run search_uniprot_gene with 'TP53'. The agent returns multiple entries, including ortholog IDs for both species, allowing immediate comparison.

02 02

Target Identification

A drug discovery team is looking for metalloproteases that convert angiotensin II. They use search_uniprot with 'metalloprotease' and 'angiotensin'. The agent pulls candidate proteins (like ACE2) and their functional annotations, narrowing down the list of viable targets.

03 03

Deep Dive Lookup

The user finds a promising protein ID (Q9BYF1). They don't want the summary; they need everything. Running get_uniprot_protein guarantees they get the full, canonical record—function, gene, location, and sequence—in one shot.

04 04

Understanding Gene Family Relationships

A researcher suspects a novel protein belongs to the BRCA1 family. They run search_uniprot_gene on 'BRCA1'. The agent returns ten entries, immediately showing all known isoforms and their associated roles in DNA repair.

The honest tradeoffs

Over-relying on keyword search

Anti-pattern

The user searches for 'p53 protein' using search_uniprot but only gets a summary. They then have to manually copy the accession ID and run another lookup.

The Fix

If you know the specific UniProt accession number, skip the general search. Always use get_uniprot_protein with the full ID for guaranteed, complete data retrieval.

Forgetting gene context

Anti-pattern

The user knows the name of a gene (e.g., 'TP53') but only uses search_uniprot, which might confuse them with related proteins not encoded by that specific gene.

The Fix

When you need to see all isoforms linked to one genetic locus, use search_uniprot_gene and pass the gene name. This locks the search scope correctly.

Mixing tools incorrectly

Anti-pattern

Trying to run a general keyword search (search_uniprot) when all they really need is the details for one specific ID.

The Fix

If you have an accession number in hand, stop. Only use get_uniprot_protein. It's faster and gives you more complete data than starting with a general keyword search.

When It Fits, When It Doesn't

Use this server if your workflow involves analyzing protein sequences, comparing orthologs across species, or identifying functional domains using canonical identifiers. If you only need basic biological information (e.g., 'What is the function of hemoglobin?'), search_uniprot works fine.

However, don't use it if your problem requires non-protein data—like metabolic pathways that aren't protein-encoded, or interactions with chemical compounds not listed in UniProt's database. If you need comparative analysis based on a gene locus, search_uniprot_gene is mandatory; otherwise, stick to the specific tool that matches your input: ID for get_uniprot_protein, keywords for search_uniprot, or genes for search_uniprot_gene.

Questions you might have

How do I use search_uniprot to find proteins by function? +

You pass the functional keyword directly into search_uniprot. For example, if you want all enzymes that process lipids, you'd input 'lipid metabolizing enzyme'. The tool returns candidates and their sequences.

What is the difference between search_uniprot and get_uniprot_protein? +

Use search_uniprot when you are guessing or researching, using a keyword like 'spike protein'. Use get_uniprot_protein only if you have a precise UniProt accession ID (like P04637) and need the complete record.

Can I find all proteins from a gene name with search_uniprot_gene? +

Yes. search_uniprot_gene is designed for exactly that. Give it the gene symbol (e.g., 'BRCA1'), and it returns every known isoform across multiple species, which is crucial for evolutionary comparison.

Does UniProt MCP Server require an API key? +

No. You don't need to worry about managing keys or endpoints; Vinkius handles the connection when you subscribe and connect your AI client.

When I use get_uniprot_protein, what detailed information do I get besides the sequence? +

You receive the full amino acid sequence alongside critical annotations. This includes functional descriptions, subcellular location data, and whether the entry is manually curated (Swiss-Prot) or auto-annotated (TrEMBL). The tool provides context necessary for experimental design.

How does search_uniprot handle non-protein keywords or general terms? +

It returns results based on matching names, functions, and gene symbols. If you use a general term like 'oxidative stress,' the tool finds proteins associated with that function, rather than requiring an exact match.

If I need to check multiple related genes, is it better to run search_uniprot_gene or search_uniprot? +

You should use search_uniprot_gene. This tool specifically compiles all protein isoforms and their annotations for a single gene across different organisms, giving you a more complete comparison set.

Are there any rate limits when I run multiple searches using the search_uniprot tool? +

While no specific limit is published here, running many requests in quick succession may trigger throttling. For large-scale comparative analyses, batching your calls or incorporating delays between API invocations is advisable.

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.

Connect to your AI in seconds.

Get uniprot protein

Search uniprot gene

Search uniprot

UniProt MCP Server: 3 Tools for Protein Data Access

Make your AI actually useful.

Get Uniprot Protein

Search Uniprot Gene

Search Uniprot

Security and governance baked right in.

Claude AI

Open Claude Settings

Add Custom Connector

Start a conversation

Claude Code

Open your terminal

Add the MCP Server

Start coding

Cursor

One-Click Install (Recommended)

Open Cursor Settings

Add New Server

Use in Composer

Antigravity

Configure Agent Environment

Bind the Endpoint

Execute

VS Code Copilot

One-Click Install (Recommended)

Open MCP Settings

Add Server Config

Windsurf

One-Click Install (Recommended)

Open Windsurf Settings

Add Server Endpoint

LangChain

Install Dependencies

Connect the Server

CrewAI

Define the Tool

Execute Task

Choose How to Get Started

Build Your Own

Make Your AI Do More

Works with Claude, ChatGPT, Cursor, and more

Finding protein data shouldn't require knowing three different API endpoints.

UniProt MCP Server: Get complete protein records with `get_uniprot_protein`

What your AI can actually do with this

Here's how it actually works

Who is this actually for?

What Changes When You Connect

See it in action

Comparing Orthologs

Target Identification

Deep Dive Lookup

Understanding Gene Family Relationships

The honest tradeoffs

Over-relying on keyword search

Forgetting gene context

Mixing tools incorrectly

When It Fits, When It Doesn't

Questions you might have