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BioMCP

by acashmoney
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TypeScript
Hybrid

BioMCP: Facilitación de la I+D biomédica basada en agentes

insignia de herreríaBioMCP

Descripción general

BioMCP es un servidor de Protocolo de Contexto de Modelo (MCP) diseñado para optimizar modelos lingüísticos extensos con capacidades de análisis de la estructura de proteínas. Proporciona herramientas para analizar los sitios activos de las proteínas y buscar proteínas relacionadas con enfermedades mediante la interacción con bases de datos de proteínas consolidadas.

El trabajo futuro se centrará en permitir que los agentes utilicen el BioMCP.

Related MCP server: PubMed MCP Server

Características

  • Análisis del sitio activo : examine los sitios de unión y los residuos funcionales de las proteínas utilizando identificadores PDB

  • Búsqueda de proteínas y enfermedades : encuentre estructuras proteicas asociadas con enfermedades o afecciones médicas específicas.

  • Acceso integrado a datos : Conéctese sin problemas con el banco de datos de proteínas RCSB (PDB)

Detalles técnicos

BioMCP implementa el Protocolo de Contexto de Modelo, lo que permite a los modelos de lenguaje acceder a información especializada sobre la estructura de las proteínas sin que esta información forme parte de sus datos de entrenamiento. El servidor gestiona las conexiones API, el formato de datos y la gestión de errores para proporcionar información fiable sobre la estructura de las proteínas.

Puntos finales de API

BioMCP expone dos herramientas principales:

  1. analyze-active-site : proporciona información detallada sobre los sitios de unión de proteínas utilizando un ID de PDB

  2. search-disease-proteins : Devuelve proteínas relacionadas con enfermedades o afecciones médicas específicas

Empezando

Instalación mediante herrería

Para instalar BioMCP para Claude Desktop automáticamente a través de Smithery :

npx -y @smithery/cli install @acashmoney/bio-mcp --client claude

Instalación manual

# Clone the repository
git clone https://github.com/acashmoney/bio-mcp.git

# Install dependencies
npm install

# Start the server
npm start

Instrucciones de configuración

Ejecución del inspector MCP

  1. Inicie el servidor BioMCP:

    npm start

  2. En una terminal separada, instale el Inspector MCP globalmente (si aún no está instalado):

    npm install -g @anthropic-ai/mcp-inspector

  3. Inicie el Inspector MCP y conéctese a su servidor BioMCP local:

    npx @modelcontextprotocol/inspector node build/index.js

  4. Utilice la interfaz del inspector para probar herramientas y ver respuestas.

Uso con Claude Desktop

  1. Construya el servidor BioMCP:

    npm run build

  2. Configurar Claude Desktop para iniciar el servidor MCP:

    a. Localice el archivo config.json de Claude Desktop (normalmente se encuentra en su directorio de usuario).

    b. Edite el archivo config.json para incluir la ruta de compilación del servidor BioMCP. Ejemplo de configuración:

    {
  "globalShortcut": "",
  "mcpServers": {
    "bio-mcp": {
      "command": "node",
      "args": [
        "/path/to/your/build/index.js"
      ]
    }
  }
}

    c. Reemplace /path/to/your/build con su ruta real al directorio del proyecto.

  3. Reinicie Claude Desktop para que los cambios surtan efecto.

  4. Ahora puedes hacerle preguntas a Claude que utilizan las herramientas BioMCP:

    • "¿Cuáles son los residuos clave en el sitio activo de la estructura 6LU7 del PDB?"

    • "Encontrar proteínas relacionadas con la enfermedad de Alzheimer"

Ejemplo de uso

Cuando se integra con un modelo de lenguaje compatible, Bio-MCP permite consultas como:

  • "¿Cuáles son los residuos clave en el sitio activo de la estructura 6LU7 del PDB?"

  • "Encontrar proteínas relacionadas con la enfermedad de Alzheimer"

Requisitos

  • Node.js 20.0.0 o superior

  • TypeScript 5.0+

  • Implementación de cliente MCP compatible

Pruebas

BioMCP incluye un conjunto integral de pruebas con pruebas unitarias, de integración y de extremo a extremo.

Ejecución de pruebas

Ejecutar todas las pruebas:

npm test

Ejecutar conjuntos de pruebas específicos:

# Unit tests only
npm run test:unit

# Integration tests only (API interactions)
npm run test:integration

# End-to-end tests only
npm run test:e2e

Pelusa

Comprobar la calidad del código:

npm run lint

Soluciona problemas de pelusa automáticamente:

npm run lint:fix

Hoja de ruta

  • Ampliar el nivel de detalle de las descripciones de sitios activos

  • Aproveche las coordenadas 3D

  • Herramientas para interactuar con la literatura

  • Herramientas para interactuar con modelos de biología computacional:

    • Difusión de radiofrecuencia

    • ProteínaMPNN

    • ColabFold

    • Herramientas adicionales de diseño y predicción de estructura de proteínas

  • Canalizaciones de investigación basadas en agentes

  • Presentar al cliente las herramientas de visualización de proteínas.

Available Tools

2 tools
analyze-active-siteC

Analyze the active site of a protein structure

ParametersJSON Schema
NameRequiredDescriptionDefault
pdbIdYesThe PDB ID of the protein structure to analyze (e.g., 6LU7)

TDQS

C2.9/5.0
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 only states the action without disclosing behavioral traits. It doesn't mention computational requirements, output format, error conditions, or whether the analysis is read-only or has side effects, leaving significant gaps.

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 zero wasted words. It's appropriately sized for a simple tool and front-loaded with the core purpose, making it easy to parse.

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 for a tool that performs analysis. It doesn't explain what the analysis entails, what results to expect, or any behavioral context, which is inadequate for guiding an AI agent effectively.

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%, so the schema already documents the single parameter 'pdbId'. The description adds no additional meaning beyond what the schema provides, such as examples of analysis outputs or constraints on PDB IDs, meeting the baseline for high 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 ('analyze') and target resource ('active site of a protein structure'), making the purpose understandable. However, it doesn't differentiate from the sibling tool 'search-disease-proteins' or specify what aspects of the active site are analyzed, preventing a perfect score.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus the sibling 'search-disease-proteins' or other alternatives. The description implies usage for protein structure analysis but lacks explicit context, prerequisites, or exclusions.

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

search-disease-proteinsC

Search for proteins related to a disease

ParametersJSON Schema
NameRequiredDescriptionDefault
diseaseYesDisease name (e.g., 'covid', 'alzheimer's')

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states what the tool does ('Search for proteins') without describing how it behaves—no information about response format, pagination, error handling, rate limits, authentication requirements, or whether it's a read-only or mutating operation. This leaves significant gaps in understanding the tool's behavior.

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 extremely concise—a single sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core functionality, making it efficient and easy to parse, though this conciseness comes at the cost of completeness in other dimensions.

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 the lack of annotations and output schema, the description is insufficiently complete. It doesn't explain what the search returns (e.g., protein names, IDs, relevance scores), how results are structured, or any behavioral aspects. For a search tool with no structured output documentation, the description should provide more context about the expected results and operational characteristics.

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 single parameter 'disease' fully documented in the schema. The description doesn't add any parameter-specific information beyond what's already in the schema (e.g., it doesn't elaborate on search algorithms, result filtering, or disease name formatting). This meets the baseline expectation when schema coverage is complete.

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 tool's purpose with a specific verb ('Search') and resource ('proteins related to a disease'), making it immediately understandable. However, it doesn't explicitly differentiate from the sibling tool 'analyze-active-site', which appears to be a different type of analysis tool rather than a direct alternative for searching.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool 'analyze-active-site' or any other potential tools, nor does it specify prerequisites, constraints, or typical use cases beyond the basic purpose.

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 updatesv1.0.0
    • Addedanalyze-active-site
    • Addedsearch-disease-proteins
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

B3/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: one analyzes protein structures (active sites), while the other searches for disease-related proteins. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the task.

Naming Consistency4/5

Both tools use a consistent verb-noun pattern with hyphens (e.g., analyze-active-site, search-disease-proteins). This naming scheme is readable and predictable, though with only two tools, it's hard to assess full consistency across a larger set.

Tool Count2/5

With only two tools, the server feels thin for a bioinformatics domain, which typically involves more operations like sequence alignment, structure prediction, or data retrieval. This limited set may not cover common workflows adequately.

Completeness2/5

The tool surface is severely incomplete for a bioinformatics server. It lacks basic operations such as fetching protein sequences, aligning sequences, predicting structures, or managing datasets. This will likely cause agent failures in handling typical tasks in this domain.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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