Busca artículos científicos desde Claude, Cursor o cualquier herramienta de IA compatible con MCP.
BGPT es un servidor remoto de Model Context Protocol (MCP) que brinda a los asistentes de IA acceso a una base de datos de artículos científicos construida a partir de estudios de texto completo. A diferencia de las herramientas de búsqueda típicas que devuelven títulos y resúmenes, BGPT extrae datos experimentales sin procesar: métodos, resultados, conclusiones, puntuaciones de calidad, tamaños de muestra, limitaciones y más de 25 campos de metadatos por artículo.
Añade BGPT a tu cliente MCP; no se requiere clave API para el nivel gratuito (50 resultados gratuitos).
La mayoría de los clientes MCP modernos admiten conexiones remotas directas. BGPT ofrece dos transportes:
Transporte | Endpoint |
SSE |
|
Streamable HTTP |
|
Claude Desktop (claude_desktop_config.json):
{
"mcpServers": {
"bgpt": {
"url": "https://bgpt.pro/mcp/sse"
}
}
}Cursor (.cursor/mcp.json):
{
"mcpServers": {
"bgpt": {
"url": "https://bgpt.pro/mcp/sse"
}
}
}Claude Code (CLI):
claude mcp add bgpt --transport sse https://bgpt.pro/mcp/sseCline / Roo Code / Windsurf — misma configuración:
{
"mcpServers": {
"bgpt": {
"url": "https://bgpt.pro/mcp/sse"
}
}
}Consejo: Si tu cliente admite Streamable HTTP, puedes usar
https://bgpt.pro/mcp/streamen su lugar.
{
"mcpServers": {
"bgpt": {
"command": "npx",
"args": ["-y", "bgpt-mcp"]
}
}
}npm install -g bgpt-mcpLuego añádelo a tu configuración de MCP:
{
"mcpServers": {
"bgpt": {
"command": "bgpt-mcp"
}
}
}Conéctate a cualquiera de los endpoints:
SSE: https://bgpt.pro/mcp/sse
Streamable HTTP: https://bgpt.pro/mcp/streamEso es todo. Sin Docker, sin pasos de compilación.
Related MCP server: mcp-spacefrontiers
BGPT proporciona una herramienta: search_papers
Parámetro | Tipo | Requerido | Descripción |
| string | Sí | Términos de búsqueda (ej. "eficiencia de edición genética CRISPR") |
| integer | No | Número de resultados a devolver (1–100, predeterminado 10) |
| integer | No | Solo devolver artículos publicados en los últimos N días |
| string | No | Tu ID de suscripción de Stripe para acceso de pago |
Cada resultado de artículo incluye más de 25 campos, extraídos del texto completo:
Título y DOI — identificadores estándar
Métodos — diseño experimental, técnicas utilizadas
Resultados — hallazgos sin procesar, mediciones, resultados estadísticos
Conclusiones — lo que determinaron los autores
Puntuaciones de calidad — evaluación del rigor metodológico
Tamaños de muestra — recuento de participantes/especímenes
Limitaciones — debilidades reconocidas
Y más — financiación, conflictos de intereses, tipo de estudio, etc.
Pregunta a tu asistente de IA:
"Busca artículos recientes sobre tasas de respuesta a la terapia de células CAR-T"
BGPT devuelve datos experimentales estructurados sobre los que tu IA puede razonar, no solo una lista de títulos.
Nivel | Coste | Detalles |
Gratuito | $0 | 50 resultados gratuitos, no se necesita clave API |
Pago por uso | $0.02/resultado | Facturado por resultado devuelto. Obtén una clave API en bgpt.pro/mcp |
Your AI Assistant (Claude, Cursor, etc.)
│
│ MCP Protocol (SSE or Streamable HTTP)
▼
BGPT MCP Server
https://bgpt.pro/mcp/sse
https://bgpt.pro/mcp/stream
│
│ search_papers(query, ...)
▼
BGPT Paper Database
(full-text extracted data)
│
▼
Structured Results
(methods, results, quality scores, 25+ fields)BGPT es un servidor remoto alojado: tu cliente MCP se conecta a través de SSE o Streamable HTTP. No se necesita instalación local.
Revisiones bibliográficas — Pide a tu IA que analice un tema con datos experimentales reales
Síntesis de evidencia — Fundamenta las respuestas de la IA en hallazgos de estudios reales
Asistencia en investigación — Encuentra artículos por metodología, resultado o actualidad
Verificación de hechos — Verifica afirmaciones frente a resultados experimentales publicados
Redacción de subvenciones — Reúne rápidamente evidencia de respaldo para propuestas
Campo | Valor |
Protocolo | MCP (Model Context Protocol) |
Transporte | SSE (Server-Sent Events) o Streamable HTTP |
Endpoint SSE |
|
Endpoint Streamable HTTP |
|
Autenticación | Ninguna requerida (nivel gratuito) / Clave API de Stripe (pago) |
{
"mcpServers": {
"bgpt": {
"url": "https://bgpt.pro/mcp/sse"
}
}
}Documentación completa, preguntas frecuentes y guías de configuración: bgpt.pro/mcp
Correo electrónico: contact@bgpt.pro
Problemas: GitHub Issues
Clave API / Facturación: bgpt.pro/mcp
Consulta CONTRIBUTING.md para obtener pautas sobre cómo informar errores, solicitar funciones y contribuir.
Este repositorio (documentación, ejemplos y archivos de configuración) tiene licencia bajo la Licencia MIT.
El servicio BGPT MCP API en sí es operado por BGPT y está sujeto a sus propios términos de servicio.
Look up a single paper by its DOI.
| Name | Required | Description | Default |
|---|---|---|---|
| doi | Yes | The DOI of the paper (e.g. "10.1038/s41586-024-07386-0"). |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, indicating a safe, idempotent operation. The description adds no extra behavioral context (e.g., response format, authentication) beyond what annotations provide.
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, direct sentence with no wasted words. It is front-loaded with the core action and resource.
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 simple tool with one parameter and an output schema, the description fully covers the functionality. The output schema eliminates the need to describe return values.
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 for the single 'doi' parameter, including an example. The description ('by its DOI') adds no additional meaning beyond what the schema already conveys.
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 ('look up') and the resource ('a single paper') using a specific identifier ('DOI'). This directly distinguishes it from the sibling tool 'search_papers', which would be used for broader searches.
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?
No guidance on when to use this tool versus alternatives. The sibling tool 'search_papers' is listed, but the description does not contrast or provide usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search BGPT's database of scientific papers by keyword.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search terms (e.g. "CRISPR gene editing efficiency") Short, concise queries are best. English language only. Don't include years or filters — use the days_back and num_results params instead. | |
| days_back | No | Only return papers published within the last N days. | |
| num_results | No | Number of results to return (1-100, default 16). First 50 results are free, then billed at $0.01/result for paid users. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint and idempotentHint. The description adds no behavioral context beyond 'search by keyword,' such as rate limits, pagination behavior, or billing details (which are in param descriptions but not the main description). Minimal additional value.
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?
Single sentence, no wasted words. However, it is very brief and could be structured to front-load key information like what the tool does, but it does so adequately.
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?
Given the tool's simplicity, parameter richness, and presence of output schema, the description is sufficiently complete. It covers the core function and leaves return value details to the output schema.
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?
Schema descriptions cover all parameters (100%). The description adds valuable usage hints beyond schema: 'Short, concise queries are best. English language only. Don't include years or filters...' This aids correct parameter use.
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?
Description clearly states the verb (Search), resource (BGPT's database of scientific papers), and method (by keyword). It distinguishes from sibling lookup_paper which is likely a direct lookup by ID.
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?
No explicit guidance on when to use this tool versus lookup_paper. The description implies use for keyword search, but does not state when not to use it or provide alternatives. Usage is implied but not clearly delineated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The two tools have entirely distinct purposes: lookup_paper retrieves a specific paper by DOI, while search_papers finds papers by keyword. There is no overlap or ambiguity.
Both tool names follow a consistent verb_noun snake_case pattern (lookup_paper, search_papers), making them predictable and readable.
With only two tools, the server feels minimal but not unreasonable for a focused paper retrieval service. However, it's on the thin side for a database named BGPT.
The server provides basic search and retrieval by DOI, covering core read operations. Missing features like author-based search, citation info, or export are notable but not critical for simple use.
Search 8.5M scientific papers with LLM TLDRs, citations, linked entities, figures, and full text.
Science MCP — free science data APIs
Biomedical data: compounds, drug info, and molecular targets
Search 340M+ academic papers — citation graphs, semantic similarity, and AI literature reviews.
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