Skip to main content
Glama
deeneskoruzh
HubLensOfficial

HubLens MCP Server

by HubLensOfficial
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

@hublens/mcp-server

HubLens 的 MCP (Model Context Protocol) 服务器 — 可在 Claude、Cursor 及其他兼容 MCP 的 AI 工具中查询热门开源项目和 AI 生成的摘要。

HubLens 每天自动检测 GitHub 和 Hacker News 上的热门开源项目，并通过 Vertex AI (Gemini) 生成中英文摘要、分类和评分。此 MCP 服务器封装了 HubLens 公共 REST API，使任何 AI 代理都能基于最新的、结构化的多日数据提供开源项目推荐。

安装

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

对于 Claude Desktop 或其他客户端，请添加到您的 MCP 配置文件中：

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

无需 API 密钥。服务器调用公共缓存端点，每个 IP 每小时限流 60 次请求。

Related MCP server: GitHub Analytics MCP Server

工具

hublens_trending

按 HubLens 评分排名的今日热门开源项目。

参数

类型

默认值

描述

limit

number (1–50)

10

结果数量

category

string

—

按类别筛选 (例如 AI, DevTools)

source

string

—

按来源筛选 (github 或 hn)

hublens_search

搜索 HubLens 跟踪的所有开源项目存档。

参数

类型

默认值

描述

q

string

—

对 slug / 标题进行文本搜索

limit

number (1–100)

20

每页结果数

offset

number

0

分页偏移量

category

string

—

类别筛选

source

string

—

来源筛选

hublens_article

通过 slug 获取完整的文章详情（中英文摘要、用例、亮点、标签、星标、类别、评分）。

参数

类型

描述

slug

string

项目 slug，例如 facebook-react

示例提示词

  • “今天有哪些热门的 AI 开源项目？” → hublens_trending(category: "AI")

  • “帮我查找 HubLens 跟踪的基于 Rust 的向量数据库。” → hublens_search(q: "vector")

  • “总结一下 HubLens 关于 langchain 的文章。” → hublens_article(slug: "langchain-ai-langchain")

数据来源

此服务器是 HubLens REST API (https://hublens.dev/api/v1/*) 的轻量级封装。无本地状态，无需凭据。有关端点详情，请参阅 API 文档。

许可证

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

Related MCP Connectors

Related MCP Servers

  • GitHub Analytics MCP Server

    Pyroxyl
    A
    license
    Not graded
    quality
    D
    maintenance
    Enables querying and analysis of public GitHub repositories for statistics, contributor data, and commit history. It provides both a RESTful API and an MCP interface for seamless integration with AI agents.
    MIT

  • ossinsight-mcp

    gztchan
    A
    license
    B
    quality
    D
    maintenance
    An MCP server that provides access to the OSS Insight Public API for GitHub repository analytics. It enables querying trending repositories, contributor statistics, and collection rankings through various tools.
    18
    10 npm
    3
    MIT