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TakumiY235

UniProt MCP Server

by TakumiY235
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Servidor MCP de UniProt

Un servidor de Protocolo de Contexto de Modelo (MCP) que proporciona acceso a la información de las proteínas de UniProt. Este servidor permite a los asistentes de IA obtener información sobre la función y la secuencia de las proteínas directamente de UniProt.

Características

  • Obtenga información sobre proteínas por número de acceso de UniProt

  • Recuperación por lotes de múltiples proteínas

  • Almacenamiento en caché para un mejor rendimiento (TTL de 24 horas)

  • Manejo y registro de errores

  • La información incluye:

    • Nombre de la proteína

    • Descripción de la función

    • Secuencia completa

    • Longitud de la secuencia

    • Organismo

Related MCP server: UniProt MCP Server

Inicio rápido

  1. Asegúrese de tener instalado Python 3.10 o superior

  2. Clonar este repositorio:

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

  3. Instalar dependencias:

    # Using uv (recommended)
uv pip install -r requirements.txt

# Or using pip
pip install -r requirements.txt

Configuración

Agregue a su archivo de configuración de Claude Desktop:

  • Ventanas: %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"]
    }
  }
}

Ejemplos de uso

Después de configurar el servidor en Claude Desktop, puede hacer preguntas como:

Can you get the protein information for UniProt accession number P98160?

Para consultas por lotes:

Can you get and compare the protein information for both P04637 and P02747?

Referencia de API

Herramientas

  1. get_protein_info

    • Obtener información para una sola proteína

    • Parámetro obligatorio: accession (número de acceso de UniProt)

    • Ejemplo de respuesta:

      {
  "accession": "P12345",
  "protein_name": "Example protein",
  "function": ["Description of protein function"],
  "sequence": "MLTVX...",
  "length": 123,
  "organism": "Homo sapiens"
}

  2. get_batch_protein_info

    • Obtenga información para múltiples proteínas

    • Parámetro obligatorio: accessions (matriz de números de acceso de UniProt)

    • Devuelve una matriz de objetos de información de proteínas.

Desarrollo

Configuración del entorno de desarrollo

  1. Clonar el repositorio

  2. Crear un entorno virtual:

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

  3. Instalar dependencias de desarrollo:

    pip install -e ".[dev]"

Ejecución de pruebas

pytest

Estilo de código

Este proyecto utiliza:

  • Negro para formato de código

  • isort para la clasificación de importaciones

  • flake8 para quitar pelusa

  • mypy para verificación de tipos

  • bandido para controles de seguridad

  • seguridad para comprobaciones de vulnerabilidad de dependencia

Ejecutar todas las comprobaciones:

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

Detalles técnicos

  • Construido con el SDK de Python de MCP

  • Utiliza httpx para solicitudes HTTP asíncronas

  • Implementa almacenamiento en caché con TTL de 24 horas utilizando un caché basado en OrderedDict

  • Maneja la limitación de velocidad y los reintentos

  • Proporciona mensajes de error detallados

Manejo de errores

El servidor gestiona varios escenarios de error:

  • Números de acceso no válidos (404 respuestas)

  • Problemas de conexión de API (errores de red)

  • Limitación de velocidad (429 respuestas)

  • Respuestas mal formadas (errores de análisis de JSON)

  • Gestión de caché (TTL y límites de tamaño)

Contribuyendo

¡Agradecemos tus contribuciones! No dudes en enviar una solicitud de incorporación de cambios. Así es como puedes contribuir:

  1. Bifurcar el repositorio

  2. Crea tu rama de funciones ( git checkout -b feature/amazing-feature )

  3. Confirme sus cambios ( git commit -m 'Add some amazing feature' )

  4. Empujar a la rama ( git push origin feature/amazing-feature )

  5. Abrir una solicitud de extracción

Asegúrese de actualizar las pruebas según corresponda y respetar el estilo de codificación existente.

Licencia

Este proyecto está licenciado bajo la licencia MIT: consulte el archivo de LICENCIA para obtener más detalles.

Expresiones de gratitud

  • UniProt por proporcionar la API de datos de proteínas

  • Antrópico para la especificación del Protocolo de Contexto del Modelo

  • Colaboradores que ayudan a mejorar este proyecto

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.

  1. 2 tool updates
    • First observedget_batch_protein_info
    • First observedget_protein_info
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.3/5.0

Scored across 2 tools

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
ResponsivenessNo issues

Resources

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