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TakumiY235

UniProt MCP Server

by TakumiY235
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

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

  • Get protein information by UniProt accession number

  • Batch retrieval of multiple proteins

  • Caching for improved performance (24-hour TTL)

  • Error handling and logging

  • Information includes:

    • Protein name

    • Function description

    • Full sequence

    • Sequence length

    • Organism

Related MCP server: UniProt MCP Server

Quick Start

  1. Ensure you have Python 3.10 or higher installed

  2. Clone this repository:

    git clone https://github.com/TakumiY235/uniprot-mcp-server.git
cd uniprot-mcp-server

  3. Install 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.json

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

  • Linux: ~/.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?

API Reference

Tools

  1. get_protein_info

    • Get 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"
}

  2. get_batch_protein_info

    • Get information for multiple proteins

    • Required parameter: accessions (array of UniProt accession numbers)

    • Returns an array of protein information objects

Development

Setting up development environment

  1. Clone the repository

  2. Create a virtual environment:

    python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

  3. Install development dependencies:

    pip install -e ".[dev]"

Running tests

pytest

Code style

This project uses:

  • Black for code formatting

  • isort for import sorting

  • flake8 for linting

  • mypy for type checking

  • bandit for security checks

  • safety for dependency vulnerability checks

Run all checks:

black .
isort .
flake8 .
mypy .
bandit -r src/
safety check

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

  • Handles rate limiting and retries

  • Provides detailed error messages

Error Handling

The server handles various error scenarios:

  • Invalid accession numbers (404 responses)

  • API connection issues (network errors)

  • Rate limiting (429 responses)

  • 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:

  1. Fork the repository

  2. Create your feature branch (git checkout -b feature/amazing-feature)

  3. Commit your changes (git commit -m 'Add some amazing feature')

  4. Push to the branch (git push origin feature/amazing-feature)

  5. 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 tools
get_batch_protein_infoB

Get protein information for multiple accession No.

ParametersJSON Schema
NameRequiredDescriptionDefault
accessionsYesList of UniProt accession No.

TDQS

B3.2/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but lacks behavioral details. It doesn't disclose whether this is a read-only operation, potential rate limits, authentication needs, or what 'protein information' includes (e.g., format, fields). The description is minimal and adds little beyond the basic action.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with no wasted words, clearly front-loading the purpose. It is appropriately sized for a simple tool with one parameter.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description is incomplete. It doesn't explain what 'protein information' entails, potential errors, or behavioral traits, leaving significant gaps for a tool that presumably returns complex data.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with the parameter 'accessions' documented as 'List of UniProt accession No.' in the schema. The description adds no additional meaning beyond this, such as format examples, constraints, or usage tips, so it meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Get protein information') and the resource ('multiple accession No.'), making the purpose understandable. It distinguishes from the sibling tool 'get_protein_info' by specifying 'multiple' vs. presumably single, though not explicitly naming the alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when multiple accession numbers are needed, but provides no explicit guidance on when to use this vs. the sibling tool 'get_protein_info' (e.g., for bulk vs. single queries). No exclusions or prerequisites are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_protein_infoB

Get protein function and sequence information from UniProt using an accession No.

ParametersJSON Schema
NameRequiredDescriptionDefault
accessionYesUniProt Accession No. (e.g., P12345)

TDQS

B3.2/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions the data source (UniProt) and type of information, but lacks details on behavioral traits like rate limits, error handling, authentication needs, or response format. This is a significant gap for a tool with no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the purpose without unnecessary words. Every part of the sentence contributes to understanding the tool's function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description is incomplete. It does not explain what the return values look like (e.g., format of function and sequence information), error cases, or other contextual details needed for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, with the parameter 'accession' well-documented in the schema. The description adds minimal value by mentioning 'UniProt Accession No.' and providing an example, but does not elaborate beyond what the schema already specifies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Get') and resource ('protein function and sequence information from UniProt'), specifying the data source and type of information retrieved. It distinguishes from the sibling tool 'get_batch_protein_info' by implying this is for single proteins, though not explicitly contrasting them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when you have a UniProt accession number and need protein details, but does not explicitly state when to use this versus the sibling batch tool or other alternatives. No exclusions or prerequisites are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updates
    • First observedget_batch_protein_info
    • First observedget_protein_info
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TDQS

B3.3/5.0
Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count2/5

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.

Completeness2/5

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

ActivityInactive
ResponsivenessSyncing

Resources

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