MCP PubMed Search
Servidor MCP de pubmed_search
buscar en pubmed a través de MCP
Componentes
Recursos
El servidor implementa un sistema de almacenamiento de notas simple con:
Nota personalizada: esquema URI para acceder a notas individuales
Cada recurso de nota tiene un nombre, una descripción y un tipo MIME de texto simple.
Indicaciones
El servidor proporciona un único mensaje:
resumir-notas: Crea resúmenes de todas las notas almacenadas
Argumento opcional "estilo" para controlar el nivel de detalle (breve/detallado)
Genera un mensaje que combina todas las notas actuales con la preferencia de estilo.
Herramientas
El servidor implementa una herramienta:
add-note: Agrega una nueva nota al servidor
Toma "nombre" y "contenido" como argumentos de cadena requeridos
Actualiza el estado del servidor y notifica a los clientes sobre los cambios de recursos.
Related MCP server: pubmed-search
Configuración
[TODO: Agregar detalles de configuración específicos para su implementación]
Inicio rápido
Instalar
Escritorio de Claude
En MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json En Windows: %APPDATA%/Claude/claude_desktop_config.json
Desarrollo
Construcción y publicación
Para preparar el paquete para su distribución:
Sincronizar dependencias y actualizar el archivo de bloqueo:
uv syncDistribuciones de paquetes de compilación:
uv buildEsto creará distribuciones de origen y de rueda en el directorio dist/ .
Publicar en PyPI:
uv publishNota: Deberás configurar las credenciales de PyPI a través de variables de entorno o indicadores de comando:
Token:
--tokenoUV_PUBLISH_TOKENO nombre de usuario/contraseña:
--username/UV_PUBLISH_USERNAMEy--password/UV_PUBLISH_PASSWORD
Depuración
Dado que los servidores MCP se ejecutan en stdio, la depuración puede ser complicada. Para una experiencia óptima, recomendamos usar el Inspector MCP .
Puede iniciar el Inspector MCP a través de npm con este comando:
npx @modelcontextprotocol/inspector uv --directory /Users/nofuture/Documents/GitHub/pubmed_search run pubmed-searchAl iniciarse, el Inspector mostrará una URL a la que podrá acceder en su navegador para comenzar a depurar.
Available Tools
1 toolpubmed_searchC
Search PubMed medical literature database
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Medical/scientific search query | |
| max_results | No | Maximum number of results (1-15) |
TDQS
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 mentions searching but doesn't describe response format, pagination, rate limits, authentication needs, or error conditions. For a search tool with zero annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
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 and front-loaded, communicating the core purpose immediately without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the search returns, how results are structured, or any behavioral constraints. The 100% schema coverage helps with parameters but doesn't compensate for the lack of output and behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, thoroughly documenting both parameters (query and max_results). The description doesn't add any parameter-specific information beyond what's already in the schema, 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search') and target resource ('PubMed medical literature database'), providing a specific verb+resource combination. However, since there are no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, preventing a perfect score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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, prerequisites, or contextual constraints. It simply states what the tool does without offering usage instructions or exclusions.
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 tool update
- First observed
pubmed_search
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool's purpose is clearly defined as searching the PubMed database, leaving no room for misselection.
A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The tool name 'pubmed_search' follows a clear verb_noun pattern that is appropriate for its function.
One tool is too few for a server named 'MCP PubMed Search', which suggests a broader scope for interacting with PubMed. A single search tool feels thin and incomplete, lacking operations like filtering results, retrieving article details, or managing queries.
The tool surface is severely incomplete for a PubMed search domain. While searching is a core function, there are obvious gaps such as no ability to get article metadata, filter searches, save results, or handle citations, which limits agent workflows significantly.
Maintenance
Related MCP Connectors
Auditable MCP server for PubMed, Europe PMC, ClinicalTrials.gov, and bioRxiv/medRxiv queries
PubMed MCP — wraps the NCBI E-utilities API (biomedical literature, free, no auth)
Search biomedical literature, get article details, find related articles, and explore MeSH terms
Related MCP Servers
- AlicenseAqualityDmaintenanceAn MCP server that provides access to PubMed articles through the Entrez API.2113 PyPI172MIT
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- AlicenseBqualityDmaintenanceThis MCP server provides 16 intelligent tools for searching, retrieving, and linking biomedical literature from PubMed and PMC. It enables LLM applications to perform complex queries, batch processing, and cross-database linking.168MIT
- AlicenseNot gradedqualityDmaintenanceThis server enables searching PubMed articles, fetching metadata, and downloading PMC PDFs.4MIT