UniProt MCP Server
The UniProt MCP Server provides access to protein function and sequence information from UniProt.
Fetch protein information: Retrieve details like protein name, function, sequence, length, and organism using a UniProt accession number.
Batch retrieval: Get information for multiple proteins at once by providing a list of accession numbers.
Caching: Improves performance with a 24-hour TTL cache.
Error handling: Manages invalid accessions, network issues, and rate limiting.
Integration: Works with AI assistants via the Model Context Protocol (MCP).
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@UniProt MCP Server get protein info for P04637"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
UniProt MCP Server
A Model Context Protocol (MCP) server that provides access to UniProt protein information. This server allows AI assistants to fetch protein function and sequence information directly from UniProt.
Features
Search UniProtKB by protein name, gene name or keyword when the accession number is not known yet
Get protein information by UniProt accession number
Batch retrieval of multiple proteins
Get annotated sequence features with their positions: signal peptide, propeptide, mature chain, active and binding sites, disulfide bonds, domains, glycosylation, natural variants and mutagenesis
Get the available structures: PDB entries with method, resolution and covered residue ranges, plus the AlphaFold DB model
Caching for improved performance (24-hour TTL), shared across the tools that read the same entry
Error handling and logging
Related MCP server: UniProt MCP Server
Requirements
Python 3.10 or higher
MCP Python SDK 1.x (
mcp>=1.1,<2)
The MCP SDK's 2.0 release removed the
@server.list_tools()/@server.call_tool()decorators from the low-levelServerclass that this project is built on. Installed againstmcp2.x, the server raisesAttributeError: 'Server' object has no attribute 'list_tools'at startup, so the dependency is pinned to the 1.x line. Migrating to the 2.x server API is tracked separately.
Quick Start
Ensure you have Python 3.10 or higher installed
Clone this repository:
git clone https://github.com/TakumiY235/uniprot-mcp-server.git cd uniprot-mcp-serverInstall dependencies:
# Using uv (recommended) uv pip install -r requirements.txt # Or using pip pip install -r requirements.txt
Configuration
Add to your Claude Desktop config file:
Windows:
%APPDATA%\Claude\claude_desktop_config.jsonmacOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonLinux:
~/.config/Claude/claude_desktop_config.json
{
"mcpServers": {
"uniprot": {
"command": "uv",
"args": ["--directory", "path/to/uniprot-mcp-server", "run", "uniprot-mcp-server"]
}
}
}Usage Examples
After configuring the server in Claude Desktop, you can ask questions like:
Can you get the protein information for UniProt accession number P98160?For batch queries:
Can you get and compare the protein information for both P04637 and P02747?When you do not have an accession number to hand:
Find the reviewed human phospholipase A2 entries and show me their lengths.Before designing a construct or a variant panel, to get the boundaries and the residues that have to be respected:
What are the signal peptide, propeptide and mature chain boundaries of P04054,
and where are its active sites and disulfide bonds?To find a structure to work from:
Which PDB structures cover P04054, and at what resolution?API Reference
Tools
get_protein_infoGet information for a single protein
Required parameter:
accession(UniProt accession number)Example response:
{ "accession": "P12345", "protein_name": "Example protein", "function": ["Description of protein function"], "sequence": "MLTVX...", "length": 123, "organism": "Homo sapiens" }
get_batch_protein_infoGet information for multiple proteins
Required parameter:
accessions(array of UniProt accession numbers)Returns an array of protein information objects
A lookup that fails is reported per accession, so one bad entry does not lose the rest of the batch
search_proteinsFind entries when the accession number is not known yet
Required parameter:
query(search terms; UniProt query syntax is accepted, e.g.gene:PLA2G1B)Optional:
organism_id(NCBI taxonomy ID, e.g.9606for human),reviewed_only(defaulttrue, Swiss-Prot only),limit(1-50, default 10)Sequences are not included, so a result list stays small
Example response:
{ "count": 1, "results": [ { "accession": "P04054", "entry_name": "PA2GA_HUMAN", "protein_name": "Phospholipase A2", "gene_names": ["PLA2G1B"], "organism": "Homo sapiens", "taxon_id": 9606, "length": 148, "reviewed": true } ] }
get_protein_featuresGet the annotated sequence features with 1-based inclusive positions
Required parameter:
accessionOptional:
types(keep only these feature types, matched case-insensitively, e.g.["Active site", "Disulfide bond"]),include_secondary_structure(defaultfalse)Secondary structure (
Helix,Beta strand,Turn) is reported per element, so an entry carries dozens of them and they say nothing a sequence-design decision needs. They are withheld unlessinclude_secondary_structureis set or they are named intypes.Example response:
{ "accession": "P04054", "length": 148, "feature_counts": {"Signal": 1, "Propeptide": 1, "Chain": 1, "Active site": 2, "Disulfide bond": 7}, "features": [ {"type": "Signal", "start": 1, "end": 15, "description": ""}, {"type": "Propeptide", "start": 16, "end": 22, "description": "Activation peptide"}, {"type": "Chain", "start": 23, "end": 148, "description": "Phospholipase A2"} ] }Features are ordered by position. A feature whose position UniProt leaves unset reports
nulland sorts last.
get_protein_structuresGet the structures available for a protein
Required parameter:
accessionExample response:
{ "accession": "P04054", "pdb_entries": [ {"pdb_id": "3ELO", "method": "X-ray", "resolution": "1.55 A", "chains": "A=16-148"}, {"pdb_id": "6Q42", "method": "X-ray", "resolution": "1.90 A", "chains": "A/B=23-148"} ], "alphafold_id": "P04054" }A protein with no solved structure reports an empty list and a null
alphafold_idrather than an error
get_protein_info, get_protein_features and get_protein_structures read
different parts of the same UniProt entry, so they share one cached request per
accession: asking for all three costs a single round trip.
Development
Setting up development environment
Clone the repository
Create a virtual environment:
python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activateInstall development dependencies:
pip install -e ".[dev]"
Running tests
pytestCode style
This project uses:
Black for code formatting
isort for import sorting
flake8 for linting
mypy for type checking
bandit for security checks
pip-audit for dependency vulnerability checks
Run all checks:
black .
isort .
flake8 .
mypy .
bandit -r src/
pip-audit .Technical Details
Built using the MCP Python SDK
Uses httpx for async HTTP requests
Implements caching with 24-hour TTL using an OrderedDict-based cache
Bounds every UniProt request with a timeout (10s connect, 30s total)
Surfaces UniProt API errors - including 429 rate limiting - as tool errors; there is no automatic retry or backoff
Provides detailed error messages
Error Handling
The server handles various error scenarios:
Invalid accession numbers (UniProt answers with 400, not 404)
API connection issues (network errors)
Rate limiting (429 responses) - reported to the caller, not retried
Malformed responses (JSON parsing errors)
Cache management (TTL and size limits)
Contributing
We welcome contributions! Please feel free to submit a Pull Request. Here's how you can contribute:
Fork the repository
Create your feature branch (
git checkout -b feature/amazing-feature)Commit your changes (
git commit -m 'Add some amazing feature')Push to the branch (
git push origin feature/amazing-feature)Open a Pull Request
Please make sure to update tests as appropriate and adhere to the existing coding style.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments
UniProt for providing the protein data API
Anthropic for the Model Context Protocol specification
Contributors who help improve this project
Available Tools
2 toolsget_batch_protein_infoB
Get protein information for multiple accession No.
Inputs: accessions (required).
get_protein_infoB
Get protein function and sequence information from UniProt using an accession No.
Inputs: accession (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: get_protein_info retrieves detailed function and sequence information for a single protein accession, while get_batch_protein_info handles multiple accessions in batch. There is no overlap or ambiguity in their functions.
Both tools follow a consistent verb_noun pattern with 'get_' prefix and snake_case naming. The naming clearly indicates the action (get) and target (protein_info), with batch differentiation for the multi-accession tool.
With only two tools, the server feels severely under-scoped for a UniProt domain. While the tools cover basic retrieval, there are obvious gaps for operations like searching, filtering, or accessing related data (e.g., taxonomy, structures), making the surface too thin for comprehensive protein information workflows.
The server is severely incomplete for UniProt functionality. It only provides protein information retrieval (single and batch), missing essential operations like search_by_keyword, get_taxonomy, get_structure, or update tracking. This will cause agent failures when trying to perform typical bioinformatics tasks beyond simple lookups.
Maintenance
Related MCP Connectors
Protein research over UniProtKB: search by function, fetch curated records, map IDs, proteomes.
UniProt protein data: find proteins, map genes to proteins, function and disease links
UniProt MCP — protein sequence + function database.
Connect AI clients to biomedical data and tools.
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