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HubLensOfficial

HubLens MCP Server

by HubLensOfficial
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@hublens/mcp-server

HubLens를 위한 MCP(Model Context Protocol) 서버 — Claude, Cursor 및 기타 MCP 호환 AI 도구에서 트렌딩 오픈 소스 프로젝트와 AI 생성 요약을 조회할 수 있습니다.

HubLens는 매일 GitHub와 Hacker News에서 트렌딩 오픈 소스(OSS)를 자동으로 감지하고, Vertex AI(Gemini)를 통해 영어/중국어 요약, 카테고리 분류 및 점수 산정을 수행합니다. 이 MCP 서버는 HubLens 공개 REST API를 래핑하여 모든 AI 에이전트가 최신의 구조화된 다일(multi-day) 데이터를 기반으로 OSS 추천을 제공할 수 있도록 합니다.

설치

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

Claude Desktop 또는 기타 클라이언트의 경우, MCP 설정에 다음을 추가하세요:

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

API 키가 필요하지 않습니다. 이 서버는 IP당 시간당 60회 요청으로 제한된 공개 캐시 엔드포인트를 호출합니다.

Related MCP server: GitHub Analytics MCP Server

도구

hublens_trending

HubLens 점수 기준 오늘 트렌딩 중인 OSS 프로젝트입니다.

매개변수

타입

기본값

설명

limit

number (1–50)

10

결과 개수

category

string

—

카테고리별 필터 (예: AI, DevTools)

source

string

—

소스별 필터 (github 또는 hn)

hublens_search

추적 중인 모든 OSS의 전체 HubLens 아카이브를 검색합니다.

매개변수

타입

기본값

설명

q

string

—

슬러그 / 제목 텍스트 검색

limit

number (1–100)

20

페이지당 결과 수

offset

number

0

페이지네이션 오프셋

category

string

—

카테고리 필터

source

string

—

소스 필터

hublens_article

슬러그를 통해 전체 아티클 세부 정보(영어+중국어 요약, 사용 사례, 하이라이트, 태그, 별점, 카테고리, 점수)를 가져옵니다.

매개변수

타입

설명

slug

string

프로젝트 슬러그 (예: facebook-react)

예시 프롬프트

  • "오늘 트렌딩 중인 AI OSS 프로젝트는 무엇인가요?" → hublens_trending(category: "AI")

  • "HubLens에서 추적하는 Rust 기반 벡터 데이터베이스를 찾아줘." → hublens_search(q: "vector")

  • "langchain에 대한 HubLens 요약을 알려줘." → hublens_article(slug: "langchain-ai-langchain")

데이터 소스

이 서버는 HubLens REST API(https://hublens.dev/api/v1/*)를 감싸는 가벼운 래퍼입니다. 로컬 상태나 자격 증명이 필요하지 않습니다. 엔드포인트 세부 정보는 API 사양을 참조하세요.

라이선스

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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