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HubLensOfficial

HubLens MCP Server

by HubLensOfficial
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JavaScript
Remote

@hublens/mcp-server

Servidor MCP (Model Context Protocol) para HubLens: consulta proyectos de código abierto en tendencia y resúmenes generados por IA desde Claude, Cursor y otras herramientas de IA compatibles con MCP.

HubLens detecta automáticamente proyectos de código abierto (OSS) en tendencia en GitHub y Hacker News diariamente, y luego genera resúmenes, categorías y puntuaciones en EN/ZH a través de Vertex AI (Gemini). Este servidor MCP envuelve la API REST pública de HubLens para que cualquier agente de IA pueda basar sus recomendaciones de OSS en datos frescos, estructurados y de varios días.

Instalación

# Claude Code
claude mcp add hublens -- npx -y @hublens/mcp-server

Para Claude Desktop u otros clientes, añádelo a tu configuración de MCP:

{
  "mcpServers": {
    "hublens": {
      "command": "npx",
      "args": ["-y", "@hublens/mcp-server"]
    }
  }
}

No se requiere clave de API. El servidor llama a endpoints públicos y cacheados con un límite de 60 peticiones/hora por IP.

Related MCP server: GitHub Analytics MCP Server

Herramientas

hublens_trending

Proyectos de OSS en tendencia hoy clasificados por la puntuación de HubLens.

Parámetro

Tipo

Predeterminado

Descripción

limit

number (1–50)

10

Número de resultados

category

string

—

Filtrar por categoría (ej. AI, DevTools)

source

string

—

Filtrar por fuente (github o hn)

hublens_search

Busca en el archivo completo de HubLens de todos los OSS rastreados.

Parámetro

Tipo

Predeterminado

Descripción

q

string

—

Búsqueda de texto en slug / título

limit

number (1–100)

20

Resultados por página

offset

number

0

Desplazamiento de paginación

category

string

—

Filtro de categoría

source

string

—

Filtro de fuente

hublens_article

Obtén los detalles completos del artículo (resúmenes en EN + ZH, casos de uso, aspectos destacados, etiquetas, estrellas, categoría, puntuación) por slug.

Parámetro

Tipo

Descripción

slug

string

Slug del proyecto, ej. facebook-react

Ejemplos de prompts

  • "¿Qué proyectos de IA OSS son tendencia hoy?" → hublens_trending(category: "AI")

  • "Búscame bases de datos vectoriales basadas en Rust rastreadas por HubLens." → hublens_search(q: "vector")

  • "Resume el artículo de HubLens para langchain." → hublens_article(slug: "langchain-ai-langchain")

Fuente de datos

Este servidor es un envoltorio ligero alrededor de la API REST de HubLens (https://hublens.dev/api/v1/*). Sin estado local, sin credenciales. Consulta la especificación de la API para obtener detalles sobre los endpoints.

Licencia

MIT © HubLens

Available Tools

3 tools
hublens_articleA

Get full details for a specific OSS project article by its slug. Includes summaries, use cases, highlights, and metadata in English and Chinese.

ParametersJSON Schema
NameRequiredDescriptionDefault
slugYesArticle slug (e.g. "facebook-react")

TDQS

A3.7/5.0
Behavior3/5

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 discloses that the tool retrieves details (implying read-only behavior) and includes multilingual content, but it does not mention potential limitations like rate limits, authentication needs, error handling, or response format. The description adds some context but lacks comprehensive behavioral traits.

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, well-structured sentence that efficiently conveys the tool's purpose, scope, and included content without unnecessary words. It is front-loaded with the core action and resource, making it easy for an agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (1 parameter, no annotations, no output schema), the description is adequate but has gaps. It covers what the tool does and what content to expect, but without annotations or output schema, it lacks details on behavioral aspects like error cases or response structure. The description is complete enough for basic use but could be more informative.

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 input schema has 100% description coverage, with the 'slug' parameter fully documented. The description adds minimal value beyond the schema by specifying the content returned (e.g., summaries, metadata in English and Chinese), but it does not provide additional details about parameter usage or constraints. Baseline 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Get full details') and resource ('specific OSS project article'), specifying the scope ('by its slug') and content included ('summaries, use cases, highlights, and metadata in English and Chinese'). It distinguishes from sibling tools hublens_search and hublens_trending by focusing on retrieving details for a specific article rather than searching or listing trending content.

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 need detailed information about a known article slug, but it does not explicitly state when to use this tool versus alternatives like hublens_search or hublens_trending. No exclusions or prerequisites are mentioned, leaving the agent to infer context from the tool name and description.

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. 3 tool updatesv0.1.1
    • First observedhublens_article
    • First observedhublens_search
    • First observedhublens_trending
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

A3.6/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: hublens_article retrieves detailed information for a specific article, hublens_search performs broad filtering and text-based searches across the archive, and hublens_trending provides ranked trending projects. There is no overlap in functionality, making tool selection straightforward for an agent.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with the prefix 'hublens_' followed by a descriptive action (article, search, trending). This uniformity enhances readability and predictability, with no deviations in style or convention.

Tool Count3/5

With only 3 tools, the server feels slightly thin for a comprehensive OSS project analysis domain, as it lacks operations like creating, updating, or deleting content. However, the tools cover core retrieval and search functions adequately for a basic service.

Completeness3/5

The tools provide good coverage for reading and searching OSS project data, but there are notable gaps in CRUD operations (e.g., no create, update, or delete tools) and limited analytical functions beyond trending. This may restrict agents from performing full lifecycle management tasks.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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