F
licenseNot graded
qualityD
maintenanceProvides access to GitHub trending projects through MCP, allowing AI assistants to query popular repositories filtered by language and time range (daily/weekly/monthly).
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HubLens MCP Server
Queries trending open-source projects detected from GitHub, providing AI agents with structured data about popular repositories including summaries, categories, and HubLens scores.
@hublens/mcp-server
MCP (Model Context Protocol) server for HubLens — query trending open-source projects and AI-generated summaries from Claude, Cursor, and other MCP-compatible AI tools.
HubLens automatically detects trending OSS on GitHub and Hacker News daily, then generates EN/ZH summaries, categories, and scoring via Vertex AI (Gemini). This MCP server wraps the HubLens Public REST API so any AI agent can ground its OSS recommendations in fresh, structured, multi-day data.
Install
# Claude Code
claude mcp add hublens -- npx -y @hublens/mcp-serverFor Claude Desktop or other clients, add to your MCP config:
{
"mcpServers": {
"hublens": {
"command": "npx",
"args": ["-y", "@hublens/mcp-server"]
}
}
}No API key required. The server calls public, cached endpoints rate-limited to 60 req/hr per IP.
Related MCP server: GitHub Analytics MCP Server
Tools
hublens_trending
Today's trending OSS projects ranked by the HubLens score.
Parameter | Type | Default | Description |
| number (1–50) | 10 | Number of results |
| string | — | Filter by category (e.g. |
| string | — | Filter by source ( |
hublens_search
Search the full HubLens archive of every tracked OSS.
Parameter | Type | Default | Description |
| string | — | Text search on slug / title |
| number (1–100) | 20 | Results per page |
| number | 0 | Pagination offset |
| string | — | Category filter |
| string | — | Source filter |
hublens_article
Fetch full article details (EN + ZH summaries, use cases, highlights, tags, stars, category, score) by slug.
Parameter | Type | Description |
| string | Project slug, e.g. |
Example prompts
"What AI OSS projects are trending today?" →
hublens_trending(category: "AI")"Find me Rust-based vector databases tracked by HubLens." →
hublens_search(q: "vector")"Summarize the HubLens writeup for langchain." →
hublens_article(slug: "langchain-ai-langchain")
Data source
This server is a thin wrapper around the HubLens REST API (https://hublens.dev/api/v1/*). No local state, no credentials. See the API spec for endpoint details.
License
MIT © HubLens
Available Tools
3 toolshublens_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
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Article 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.
hublens_searchB
Search the HubLens archive of all historically tracked OSS projects. Supports filtering by category, source, and text search.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Search query (matches slug and title) | |
| limit | No | Number of results (max 100) | |
| offset | No | Skip N results for pagination | |
| category | No | Filter by category | |
| source | No | Filter by source |
TDQS
B3.2/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions filtering and text search but fails to describe key behaviors such as pagination handling (implied by 'offset' parameter), rate limits, authentication needs, or what the search returns (e.g., list of projects with fields). This leaves significant gaps for an agent to understand operational 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, efficient sentence that front-loads the core purpose ('Search the HubLens archive...') and succinctly adds filtering details. There is no wasted text, making it highly concise and well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a search tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain the return format (e.g., what fields are included in results), error handling, or behavioral constraints like rate limits. This leaves the agent with insufficient context to use the tool effectively beyond basic parameter passing.
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?
Schema description coverage is 100%, with each parameter clearly documented in the schema (e.g., 'q' matches slug and title, 'limit' has max 100). The description adds minimal value beyond this, only listing the filter types ('category, source, and text search') without explaining semantics like what categories or sources are available. 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Search') and resource ('HubLens archive of all historically tracked OSS projects'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'hublens_article' or 'hublens_trending', which likely have different functions (e.g., retrieving specific articles or trending projects).
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 for searching with filtering capabilities ('Supports filtering by category, source, and text search'), but provides no explicit guidance on when to use this tool versus alternatives like the sibling tools. It lacks context on exclusions or prerequisites, leaving the agent to infer usage based on the search functionality.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
hublens_trendingA
Get today's trending open-source projects from HubLens. Returns ranked projects with scores, stars, categories, and AI-generated summaries.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of results (max 50) | |
| category | No | Filter by category (e.g. "AI", "DevTools") | |
| source | No | Filter by source ("github" or "hn") |
TDQS
A3.5/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It effectively describes the core behavior (returns ranked trending projects with specific data fields) and implies read-only operation through 'Get', but doesn't address important aspects like rate limits, authentication requirements, data freshness, or error conditions. It provides basic functional context but lacks operational details.
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 perfectly concise - a single sentence that efficiently communicates the tool's purpose and return value. Every word earns its place, with no redundant information. It's front-loaded with the core functionality and follows with useful details about the response format.
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?
For a read-only tool with 3 optional parameters and no output schema, the description provides adequate functional context but lacks operational details. It explains what the tool does and what data it returns, but doesn't address authentication, rate limits, error handling, or data freshness. Given the absence of annotations and output schema, a more complete description would help the agent understand operational constraints.
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?
Schema description coverage is 100%, so the schema already fully documents all three parameters (limit, category, source). The description adds no additional parameter information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in description.
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 specific action ('Get today's trending open-source projects'), identifies the resource ('from HubLens'), and distinguishes from siblings by focusing on trending projects rather than articles or general search. It provides concrete details about what's returned (ranked projects with scores, stars, categories, AI-generated summaries).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus the sibling tools (hublens_article, hublens_search). It doesn't mention alternatives, prerequisites, or specific contexts where this tool is preferred over others. The user must infer usage from the tool name and description alone.
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.
3 tool updates
v0.1.1- First observed
hublens_article - First observed
hublens_search - First observed
hublens_trending
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
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