by josefdc
UniProt — access complete UniProtKB protein entries, sequences, filtered searches and ID mapping across 200+ databases f
Provides access to UniProtKB protein database for querying protein entries, sequences, annotations, and converting between 200+ database identifier types.
UniProt is a community-built MCP server published by josefdc that provides AI assistants with tools and capabilities via the Model Context Protocol. UniProt — access complete UniProtKB protein entries, sequences, filtered searches and ID mapping across 200+ databases f It is categorized under developer tools, ai ml. This server exposes 5 tools that AI clients can invoke during conversations and coding sessions.
You can install UniProt in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
UniProt is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
Strong directory entry: UniProt surfaces stars and publisher context so we could sanity-check maintenance before adopting.
I recommend UniProt for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
I recommend UniProt for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
According to our notes, UniProt benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Strong directory entry: UniProt surfaces stars and publisher context so we could sanity-check maintenance before adopting.
UniProt is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Useful MCP listing: UniProt is the kind of server we cite when onboarding engineers to host + tool permissions.
We evaluated UniProt against two servers with overlapping tools; this profile had the clearer scope statement.
I recommend UniProt for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Strong directory entry: UniProt surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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A Model Context Protocol (MCP) server that provides seamless access to UniProtKB protein data. Query protein entries, sequences, Gene Ontology annotations, and perform ID mappings through a typed, resilient interface designed for LLM agents.
<a href="https://glama.ai/mcp/servers/@josefdc/Uniprot-MCP"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@josefdc/Uniprot-MCP/badge" alt="UniProt Server MCP server" /> </a>
pip install uniprot-mcp
Local development (stdio):
uniprot-mcp
Remote deployment (HTTP):
uniprot-mcp-http --host 0.0.0.0 --port 8000
The HTTP server provides:
http://localhost:8000/mcphttp://localhost:8000/healthzhttp://localhost:8000/metrics (Prometheus format)npx @modelcontextprotocol/inspector uniprot-mcp
Access static or dynamic data through URI patterns:
| URI | Description |
|---|---|
uniprot://uniprotkb/{accession} | Raw UniProtKB entry JSON for any accession |
uniprot://help/search | Documentation for search query syntax |
Execute actions and retrieve typed data:
| Tool | Parameters | Returns | Description |
|---|---|---|---|
fetch_entry | accession, fields? | Entry | Fetch complete protein entry with all annotations |
get_sequence | accession | Sequence | Get protein sequence with length and metadata |
search_uniprot | query, size, reviewed_only, fields?, sort?, include_isoform | SearchHit[] | Full-text search with advanced filtering |
map_ids | from_db, to_db, ids | MappingResult | Convert identifiers between 200+ databases |
fetch_entry_flatfile | accession, version, format | string | Retrieve historical entry versions (txt/fasta) |
Progress tracking: map_ids reports progress (0.0 → 1.0) for long-running jobs.
Pre-built templates for common workflows:
| Variable | Default | Description |
|---|---|---|
UNIPROT_ENABLE_FIELDS | unset | Request minimal field subsets to reduce payload size |
UNIPROT_LOG_LEVEL | info | Logging level: debug, info, warning, error |
UNIPROT_LOG_FORMAT | plain | Log format: plain or json |
UNIPROT_MAX_CONCURRENCY | 8 | Max concurrent UniProt API requests |
MCP_HTTP_HOST | 0.0.0.0 | HTTP server bind address |
MCP_HTTP_PORT | 8000 | HTTP server port |
MCP_HTTP_LOG_LEVEL | info | Uvicorn log level |
MCP_HTTP_RELOAD | 0 | Enable auto-reload: 1 or true |
MCP_CORS_ALLOW_ORIGINS | * | CORS allowed origins (comma-separated) |
MCP_CORS_ALLOW_METHODS | GET,POST,DELETE | CORS allowed methods |
MCP_CORS_ALLOW_HEADERS | * | CORS allowed headers |
# HTTP server flags
uniprot-mcp-http --host 127.0.0.1 --port 9000 --log-level debug --reload
# Using MCP client
result = await session.call_tool("fetch_entry", {
"accession": "P12345"
})
# Returns structured Entry with:
# - primaryAccession, protein names, organism
# - sequence (length, mass, sequence string)
# - features (domains, modifications, variants)
# - GO annotations (biological process, molecular function, cellular component)
# - cross-references to other databases
# Search reviewed human proteins
result = await session.call_tool("search_uniprot", {
"query": "kinase AND organism_id:9606",
"size": 50,
"reviewed_only": True,
"sort": "annotation_score"
})
# Returns list of SearchHit objects with accessions and scores
# Convert UniProt IDs to PDB structures
result = await session.call_tool("map_ids", {
"from_db": "UniProtKB_AC-ID",
"to_db": "PDB",
"ids": ["P12345", "Q9Y6K9"]
})
# Returns MappingResult with successful and failed mappings
# Clone the repository
git clone https://github.com/josefdc/Uniprot-MCP.git
cd Uniprot-MCP
# Install dependencies
uv sync --group dev
# Install development tools
uv tool install ruff
uv tool install mypy
# Run all tests with coverage
uv run pytest --maxfail=1 --cov=uniprot_mcp --cov-report=term-missing
# Run specific test file
uv run pytest tests/unit/test_parsers.py -v
# Run integration tests only
uv run pytest tests/integration/ -v
# Lint
uv tool run ruff check .
# Format
uv tool run ruff format .
# Type check
uv tool run mypy src
# Run all checks
uv tool run ruff check . && \
uv tool run ruff format --check . && \
uv tool run mypy src && \
uv run pytest
# Stdio server
uv run uniprot-mcp
# HTTP server with auto-reload
uv run python -m uvicorn uniprot_mcp.http_app:app --reload --host 127.0.0.1 --port 8000
src/uniprot_mcp/
├── adapters/ # UniProt REST API client and response parsers
│ ├── uniprot_client.py # HTTP client with retry logic
│ └── parsers.py # Transform UniProt JSON → Pydantic models
├── models/
│ └── domain.py # Typed data models (Entry, Sequence, etc.)
├── server.py # MCP stdio server (FastMCP)
├── http_app.py # MCP HTTP server (Starlette + CORS)
├── prompts.py # MCP prompt templates
└── obs.py # Observability (logging, metrics)
tests/
├── unit/ # Unit tests for parsers, models, tools
├── integration/ # End-to-end tests with VCR fixtures
└── fixtures/ # Test data (UniProt JSON responses)
This server is published to:
# Build distribution packages
uv build
# Publish to PyPI (requires token)
uv publish --token pypi-YOUR_TOKEN
# Publish to MCP Registry (requires GitHub auth)
mcp-publisher login github
mcp-publisher publish
See docs/registry.md for detailed registry publishing instructions.
Contributions are welcome! Please:
Quick start for contributors:
git checkout -b feature/amazing-feature)uv tool run ruff check . && uv tool run mypy src && uv run pytestfeat:, fix:, docs:, etc.)This project is licensed under the MIT License - see the LICENSE file for details.
This is an independent project and is not officially affiliated with or endorsed by the UniProt Consortium. Please review UniProt's terms of use when using their data.
Built with ❤️ for the bioinformatics and AI communities
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.