Suppr (超能文献) es una plataforma de herramientas académicas impulsada por IA de WildData. Este servidor MCP aporta capacidades de traducción de documentos y búsqueda de literatura a los asistentes de IA.
🌐 Traducción de documentos por IA — Traduce documentos PDF, Word (.docx), Excel (.xlsx), PowerPoint (.pptx), TXT y HTML en 13 idiomas. Conserva el formato original. Detección automática del idioma de origen.
🔬 Búsqueda académica en PubMed — Descubrimiento semántico de literatura en millones de artículos de investigación biomédica. Devuelve metadatos estructurados: DOI, PMID, factor de impacto de la revista, recuento de citas, afiliaciones de autores, resúmenes y enlaces directos a los artículos.
🤖 Compatible con MCP — Funciona con Claude Desktop, Cursor, Windsurf y cualquier cliente del Protocolo de Contexto de Modelo.
npx suppr-mcpRelated MCP server: Paperlib MCP
Instalación global:
npm install -g suppr-mcpO utilice npx (sin necesidad de instalación):
npx suppr-mcpVisite Suppr API para obtener su clave de API.
export SUPPR_API_KEY=your_api_key_hereEdite ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) o el archivo de configuración correspondiente:
{
"mcpServers": {
"suppr": {
"command": "npx",
"args": ["-y", "suppr-mcp"],
"env": {
"SUPPR_API_KEY": "your_api_key_here"
}
}
}
}O utilice la instalación global:
{
"mcpServers": {
"suppr": {
"command": "suppr-mcp",
"env": {
"SUPPR_API_KEY": "your_api_key_here"
}
}
}
}Crea una tarea de traducción de documentos.
Parámetros:
file_path (elija entre file_path y file_url): Ruta del archivo de origen
file_url (elija entre file_path y file_url): URL del documento a traducir
to_lang (obligatorio): Código del idioma de destino
from_lang (opcional): Código del idioma de origen (detección automática por defecto)
optimize_math_formula (opcional): Optimizar fórmulas matemáticas (solo PDF)
Ejemplo:
{
"file_url": "https://example.com/document.pdf",
"to_lang": "en",
"from_lang": "zh",
"optimize_math_formula": true
}Respuesta:
{
"task_id": "02a6c6d1-3f70-4a5a-80bc-971d53a37bb1",
"status": "INIT",
"consumed_point": 453,
"source_lang": "zh",
"target_lang": "en",
"optimize_math_formula": true
}Obtiene información detallada y el estado de una tarea de traducción.
Parámetros:
task_id (obligatorio): ID de la tarea de traducción
Ejemplo:
{
"task_id": "02a6c6d1-3f70-4a5a-80bc-971d53a37bb1"
}Respuesta:
{
"task_id": "02a6c6d1-3f70-4a5a-80bc-971d53a37bb1",
"status": "DONE",
"progress": 1.0,
"consumed_point": 453,
"source_file_name": "document.pdf",
"source_file_url": "https://example.com/source.pdf",
"target_file_url": "https://example.com/translated.pdf",
"source_lang": "zh",
"target_lang": "en",
"error_msg": null,
"optimize_math_formula": true
}Descripción del estado de la tarea:
INIT: Inicializado
PROGRESS: En progreso
DONE: Completado
ERROR: Error
Obtiene una lista de tareas de traducción, admite paginación.
Parámetros:
offset (opcional): Desplazamiento de paginación, por defecto 0
limit (opcional): Cantidad por página, por defecto 20
Ejemplo:
{
"offset": 0,
"limit": 10
}Respuesta:
{
"total": 42,
"offset": 0,
"limit": 10,
"list": [
{
"task_id": "...",
"status": "DONE",
"progress": 1.0,
...
}
]
}Búsqueda semántica de literatura impulsada por IA.
Parámetros:
query (obligatorio): Consulta en lenguaje natural
topk (opcional): Cantidad máxima de resultados (1-100, por defecto 20)
return_doc_keys (opcional): Especificar campos de retorno
auto_select (opcional): Selección automática de los mejores resultados (por defecto true)
Ejemplo:
{
"query": "糖尿病最新研究进展",
"topk": 5,
"return_doc_keys": ["title", "abstract", "doi", "authors"],
"auto_select": true
}Campos de retorno disponibles:
title: Título
abstract: Resumen
authors: Lista de autores
doi: DOI
pmid: ID de PubMed
link: Enlace
publication: Publicación
pub_year: Año de publicación
Consulte la documentación de la API para más campos
Respuesta:
{
"search_items": [
{
"doc": {
"title": "...",
"abstract": "...",
"authors": [...],
"doi": "...",
...
},
"search_gateway": "pubmed"
}
],
"consumed_points": 20
}Códigos de idiomas comunes:
en: Inglés
zh: Chino
ko: Coreano
ja: Japonés
fr: Francés
de: Alemán
es: Español
ru: Ruso
ar: Árabe
pt: Portugués
it: Italiano
auto: Detección automática
Todos los errores devuelven un formato estándar:
{
"code": 非零错误码,
"msg": "错误信息",
"data": null
}Errores comunes:
401: Clave de API inválida o no proporcionada
400: Error en los parámetros de la solicitud
404: El recurso no existe
Después de configurar la clave de API, reinicie Claude Desktop
Utilice las herramientas en la conversación:
Traducir documento:
Por favor, ayúdame a traducir este documento: https://example.com/paper.pdf, al inglés
Buscar literatura:
Ayúdame a buscar la literatura más reciente sobre "aplicaciones del aprendizaje profundo en imágenes médicas"
Consultar estado de traducción:
Comprobar el progreso de la traducción de la tarea 02a6c6d1-3f70-4a5a-80bc-971d53a37bb1
R: Visite https://suppr.wilddata.cn/api-keys para registrarse y obtener su clave.
R: Se admiten formatos comunes como PDF, DOCX, PPTX, XLSX, HTML, TXT, EPUB, etc.
R: Depende del tamaño del documento, generalmente desde unos minutos hasta más de diez minutos. Puede usar get_translation para consultar el progreso.
R: Una vez completada la traducción, get_translation devolverá target_file_url, acceda directamente a ese enlace para descargar.
R: Asegúrese de que la versión de Node.js sea >= 18.0.0 y que la variable de entorno SUPPR_API_KEY esté configurada.
Plugin de Zotero : https://github.com/WildDataX/suppr-zotero-plugin
Sitio web oficial: https://suppr.wilddata.cn
Traducción de documentos por IA: https://suppr.wilddata.cn/translate/upload
Servicio API: https://openapi.suppr.wilddata.cn/introduction
Búsqueda en Pubmed: https://suppr.wilddata.cn/
Investigación profunda: https://suppr.wilddata.cn/deep-research
Organización de GitHub: WildDataX
Si necesita ayuda, contacte a: IT@wilddata.cn
Hecho con ❤️ por WildData
Producto | Enlace |
🌐 Plataforma Suppr | |
📖 Documentación API | |
🔌 Plugin Zotero | |
🤖 Claude Code Skills | |
🔬 Investigación profunda | |
📄 Traducción IA | |
🔎 Búsqueda PubMed |
Create a document translation task. Supports file upload via path or URL.
| Name | Required | Description | Default |
|---|---|---|---|
| to_lang | Yes | Target language code (required), e.g., en, zh, ko, ja | |
| file_url | No | Document URL to translate (mutually exclusive with file_path) | |
| file_path | No | Local file path to translate (mutually exclusive with file_url) | |
| from_lang | No | Source language code (optional, auto-detect if not specified) | |
| optimize_math_formula | No | Optimize math formulas (PDF only) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only mentions file upload capability and does not disclose side effects (e.g., task creation, asynchronous processing), required permissions, or how to track the resulting task.
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 two short sentences, front-loaded with the purpose and immediately following with input constraints. No unnecessary words or redundancy.
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 create operation with no output schema and no annotations, the description lacks details about the response (e.g., task ID), follow-up steps (e.g., use get_translation to check status), or any workflow context. This leaves the agent uncertain about what happens after invocation.
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 description coverage is 100%, so baseline is 3. The description adds minimal meaning beyond the schema; 'file upload via path or URL' summarizes the file_path/file_url mutual exclusion, but this is already documented in the schema. No extra parameter context is provided.
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?
Clearly states the verb 'create' and resource 'document translation task', making it distinct from sibling tools (get_translation, list_translations, search_documents). The added 'Supports file upload via path or URL' further specifies the tool's scope.
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 siblings. There is no mention of get/list for retrieving tasks or search_documents for finding documents. The usage is only implied by the tool's name/verb, not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get translation task details and status. Use this to check progress and get result URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes | Translation task ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden. It implies a read-only operation via the verb 'Get' and mentions the return content (details, status, result URLs), but it does not explicitly state the absence of side effects or address error cases. More explicit transparency would be beneficial.
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 two concise sentences, front-loaded with the core purpose followed by a usage hint. Every word earns its place with no redundant content.
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 1-parameter read tool with no output schema, the description covers the essential aspects: what it does, when to use it, and what it returns (details, status, result URLs). It does not enumerate possible statuses, but this is not critical for invoking the tool correctly.
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 schema already provides a description for task_id ('Translation task ID') with 100% coverage. The tool description adds no further meaning about how to obtain or format the task_id, so it does not exceed the baseline.
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 states a specific verb ('Get') and resource ('translation task details and status'), clearly distinguishing this from sibling tools like create_translation, list_translations, and search_documents. It focuses on a single task's details, making its purpose unambiguous.
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 explicit usage context: 'Use this to check progress and get result URLs.' This tells the agent when to invoke the tool, though it doesn't contrast with alternatives or state exclusions. Given the sibling tools, this is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List translation tasks with pagination. View all historical translation tasks.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Results per page (default: 20) | |
| offset | No | Pagination offset (default: 0) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden. It transparently indicates a read-only list operation with pagination, but adds no details on ordering, status scope, or return format. The term 'historical' is ambiguous and could mislead about whether in-progress tasks are included.
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?
Two short sentences with no fluff. The first sentence states the action and key feature (pagination); the second adds the 'historical' scope. Efficient and front-loaded.
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 2-parameter list tool with no output schema or annotations, the description gives enough to understand the operation. However, it does not explain what data is returned, what 'historical' means, or how it relates to sibling search_documents, leaving some gaps.
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 already provides 100% coverage with clear descriptions for 'limit' and 'offset'. The description's mention of pagination adds marginal value beyond the schema, so the baseline score of 3 is appropriate.
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 tool lists translation tasks and supports pagination. 'List' sharply contrasts with sibling 'get_translation' and 'create_translation', making it easy for an agent to select it for multi-result retrieval.
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?
It conveys the core use case: listing all historical tasks with pagination. While it does not explicitly name alternatives or exclusions, the context 'view all historical translation tasks' gives clear guidance for when to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
AI-driven semantic search for academic literature. Input natural language query to find relevant papers.
| Name | Required | Description | Default |
|---|---|---|---|
| topk | No | Max results to return (1-100, default: 20) | |
| query | Yes | Natural language query, e.g., "latest diabetes research" | |
| auto_select | No | Auto-select best results (default: true) | |
| return_doc_keys | No | Specific fields to return, e.g., ["title", "abstract", "doi"] |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It reveals that the search is 'AI-driven' and 'semantic,' which is useful, but it does not mention whether the operation is read-only, requires authentication, or what the response contains (e.g., list of papers, metadata). For a search tool, this is a notable gap.
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 one concise sentence that front-loads the core purpose. Every word contributes meaning without redundancy. It is appropriately sized for a straightforward search tool.
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?
The tool is relatively simple with four parameters all documented in the schema, and the description covers the primary purpose. However, without annotations or an output schema, the description omits behavioral details like return format, pagination, or limitations. It is minimally complete but leaves open questions.
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 description coverage is 100%, so the baseline is 3. The description itself does not add any parameter-specific meaning beyond the schema; it only says to input a natural language query. The schema already documents each parameter thoroughly, so the description's lack of parameter detail is acceptable.
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 tool performs AI-driven semantic search for academic literature, using natural language queries to find relevant papers. It specifies the verb (search), resource (academic literature), and how to invoke it. However, it does not explicitly differentiate from sibling tools, though the siblings are translation-focused, so differentiation is apparent from context.
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 implies usage: input a natural language query to find papers. It gives a basic how-to but does not outline when to use this tool versus alternatives or state any exclusions. The sibling tools are translation-related, implying search is for finding papers, but no explicit guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.1.7create_translationget_translationlist_translationssearch_documentsScored across 4 tools
Each tool has a clear, distinct purpose: create, get, and list translations, plus search documents. There is no overlap or ambiguity between them.
All tool names follow a consistent verb_noun pattern: create_translation, get_translation, list_translations, and search_documents. No mixed conventions.
Four tools is a reasonable size for a server handling translation tasks and document search. It feels slightly minimal but each tool serves a distinct purpose.
The translation lifecycle covers create, get, and list, but lacks update/delete/cancel operations. The search_documents tool seems unrelated to translations, creating a mixed domain with notable gaps.
Translate PDFs, scans, Office files and EPUB across 475 languages and styles, preserving layout.
Search 36M+ PubMed biomedical articles and ClinicalTrials.gov studies.
Search PubMed/Europe PMC, fetch articles and full text (PMC/EPMC/Unpaywall), citations, MeSH terms.
Turn documents into structured, AI-ready data by parsing, enriching, chunking, and embedding.
