Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@HN-MCP show me the top trending stories on Hacker News"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server that enables Claude Desktop and other AI assistants to browse Hacker News, search discussions, and analyze tech trends. Clean, fast, and actually works - no API keys required.
Related MCP server: HackerNews MCP Server
🚀 Zero setup - Works instantly! Unlike Reddit, Twitter, or other platforms, Hacker News API requires no authentication, no API keys, no registration. Just install and go.
⚡ Smart caching - 50MB LRU cache with adaptive TTLs delivers 50ms cached responses (vs 200-500ms uncached). Popular content stays cached longer automatically.
🎯 Clean data - Direct from HN's Firebase API and Algolia Search. No fake metrics, no made-up sentiment scores, no hallucinated data.
🧠 LLM-optimized - Response formats designed specifically for AI assistants. Structured JSON that Claude can parse perfectly every time.
📦 TypeScript - 100% TypeScript with strict typing. Zod schemas validate every input and output. Reliable and maintainable.
✅ Multi-channel distribution - Available via npm, Docker Hub, MCP Registry, and .mcpb extension. Choose your preferred installation method.
🐳 Docker ready - Multi-platform images (amd64, arm64) with health checks and optimized builds. Deploy anywhere in seconds.
Add this to your claude_desktop_config.json:
{
"mcpServers": {
"hn": {
"command": "npx",
"args": ["hn-mcp"]
}
}
}That's it! HN-MCP is now available in Claude.
Ask your AI assistant to:
📊 "What's trending on Hacker News?" - Browse top stories
🔍 "Search for discussions about AI" - Search across all content
💬 "Get comments on story 12345678" - Fetch full discussion threads
👤 "Analyze user pg" - Get user karma, submissions, and activity
📚 "Explain HN karma" - Understand Hacker News terminology
🚀 Startup founders - Monitor YC companies, track competitor launches, discover market trends
💼 Tech recruiters - Find talented developers, understand the tech community pulse
📊 Market researchers - Analyze tech sentiment, identify emerging technologies, track discussion trends
✍️ Content creators - Find trending topics, research technical subjects, engage with developer community
🔍 Developers - Stay updated on latest tools, frameworks, and best practices discussed by peers
📰 Tech journalists - Source breaking stories, find expert opinions, track industry discussions
🎓 Researchers - Analyze tech community behavior, study information diffusion, collect discussion data
browse_storiesBrowse posts from Hacker News by category.
- Types: top, new, best, ask (Ask HN), show (Show HN), job
- Limit: 1-100 stories (default: 30)
- Returns: Title, score, author, comments, URLsearch_hnSearch across all Hacker News content.
- Query: Your search terms
- Filter by: Type, date range, author
- Sort by: relevance or date
- Uses: Algolia's HN Search APIget_story_detailsGet a story with all its comments.
- Input: Story ID
- Returns: Full story + nested comment threads
- Options: Max comments, thread depthuser_analysisAnalyze a Hacker News user's profile.
- Username: Any HN user
- Returns: Karma, account age, recent submissions
- Insights: Activity patterns, top postshn_explainGet explanations of HN terms.
- Terms: karma, flagged, dupe, Show HN, Ask HN, etc.
- Returns: Definition + contextEasiest method - One-click install for Claude Desktop:
Download hn-mcp.mcpb from latest release
Open Claude Desktop
Click on the extension file to install
Restart Claude Desktop
That's it! HN-MCP will be available immediately.
# Claude Desktop - add to config (see Quick Start above)
npx hn-mcp
# HTTP mode for testing
HN_MCP_HTTP=true npx hn-mcpnpm install -g hn-mcp
hn-mcp # Run in stdio mode
HN_MCP_HTTP=true hn-mcp # Run in HTTP mode# Quick start - HTTP mode for API access
docker run -e HN_MCP_HTTP=true -p 3000:3000 karanb192/hn-mcp:latest
# stdio mode for development
docker run -it karanb192/hn-mcp:latest
# With docker-compose
docker-compose upgit clone https://github.com/karanb192/hn-mcp.git
cd hn-mcp
npm install
npm run build
npm linkAPI | Official Limit | Our Limit | Notes |
HN Firebase | None ✅ | 300/min | Self-imposed for courtesy |
Algolia Search | None ✅ | 300/min | Max 1000 results per query |
❌ Scoped npm names - @someone/hn-something is hard to remember
❌ Not published to npm - Manual installation only
❌ No caching - Slow repeated requests
❌ Poor documentation - Unclear how to use
✅ Clean package name - Just hn-mcp
✅ Smart caching - 50MB LRU cache with adaptive TTLs
✅ Clear documentation - You're reading it
✅ TypeScript - Type-safe and maintainable
"What are the top posts about GPT-4 today?"
→ search_hn with query="GPT-4", dateRange="last24h", sortBy="relevance""Show me what's trending on Hacker News"
→ browse_stories with type="top", limit=10"What are people saying about this article?"
→ search_hn with the article URL to find discussions"Analyze the user dang"
→ user_analysis with username="dang""Get the comments from HN story 12345678"
→ get_story_details with id="12345678""Command not found" error
# Ensure npm is installed
node --version
npm --version
# Try with full npx path
$(npm bin -g)/hn-mcpConnection issues
Hacker News may be down (check https://status.ycombinator.com)
Firewall blocking requests
Try restarting the MCP server
Search returns max 1000 results
This is Algolia's hard limit
Use date filters to narrow results
Try more specific search queries
Configure HN-MCP using environment variables. See .env.example for detailed documentation.
Variable | Description | Default |
| Run as HTTP server instead of stdio |
|
| HTTP server port |
|
| Disable caching for real-time data |
|
| Override rate limit (requests/minute) |
|
Quick setup:
# Copy example file
cp .env.example .env
# Edit with your preferences
nano .env# Install dependencies
npm install
# Run in development
npm run dev
# Build
npm run build
# Run tests
npm run typecheck
# Deploy locally
./deploy-server.sh # Port 3000
./deploy-server.sh -p 8080 # Custom port
# Docker build
docker build -t karanb192/hn-mcp .
docker run -e HN_MCP_HTTP=true -p 3000:3000 karanb192/hn-mcp
# Docker development with hot reload
docker-compose --profile dev upNode.js >= 18.0.0
npm or yarn
TypeScript 5.5+
PRs welcome! See CONTRIBUTING.md for guidelines.
We keep things simple:
Clean TypeScript code
Smart caching
Clear documentation
Fast responses
MIT - Use it however you want!
Made with ❤️ for the MCP community by the creator of reddit-mcp-buddy. No venture capital, no tracking, just a good MCP server.
Browse Hacker News stories by type. Returns story list with scores, comments, and metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Story type: "top" (highest voted), "new" (most recent), "best" (curated best), "ask" (Ask HN), "show" (Show HN), "job" (Who is hiring) | top |
| limit | No | Default 30, range (1-100). Change ONLY IF user specifies. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return format but does not cover important aspects such as whether this is a read-only operation, potential rate limits, authentication needs, or how results are paginated/ordered. This leaves significant gaps for a tool that fetches data.
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 a single, efficient sentence that front-loads the core purpose and return value without any wasted words. It is appropriately sized for the tool's complexity and gets straight to the point.
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?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and return format but lacks details on behavioral traits and usage context, which are important for an agent to operate effectively without structured annotations.
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 has 100% description coverage, clearly documenting both parameters with enums and constraints. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline of 3 without compensating for any gaps.
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 verb ('browse') and resource ('Hacker News stories by type'), and specifies what is returned ('story list with scores, comments, and metadata'). However, it does not explicitly differentiate from sibling tools like 'search_hn' or 'get_story_details', which would be needed for a score of 5.
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 no guidance on when to use this tool versus alternatives like 'search_hn' or 'get_story_details'. It mentions the tool's function but lacks explicit context, exclusions, or comparisons with siblings, leaving the agent without clear usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a Hacker News story with its comments. Fetches full story content and comment threads.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Story ID from HN URL (e.g., 38765432 from item?id=38765432) | |
| maxComments | No | Default 10, range (1-50). Change ONLY IF user specifies. | |
| commentDepth | No | Default 3, range (1-5). Change ONLY IF user asks. |
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 mentions fetching 'full story content and comment threads', which suggests a read-only operation, but doesn't disclose behavioral traits like rate limits, authentication needs, error handling, or response format. For a tool with no annotation coverage, this is a significant gap in transparency.
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 that are front-loaded with the core purpose. Every sentence earns its place by specifying what is fetched and the scope (story and comments), with no redundant or vague language.
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?
Given no annotations and no output schema, the description is incomplete. It doesn't explain what the return values look like (e.g., structure of story and comments), error conditions, or other contextual details needed for effective use. For a tool fetching complex data like comment threads, this is inadequate.
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 schema already documents all parameters thoroughly. The description doesn't add any meaning beyond what the schema provides (e.g., it doesn't explain how 'maxComments' or 'commentDepth' affect the output). Baseline 3 is appropriate when the schema does the heavy lifting.
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 verb 'Get' and resource 'Hacker News story with its comments', specifying it fetches 'full story content and comment threads'. It distinguishes from siblings like 'browse_stories' (likely listing) and 'search_hn' (searching) by focusing on retrieving a specific story's details.
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 when a specific story's details are needed, but doesn't explicitly state when to use this tool versus alternatives like 'browse_stories' or 'search_hn'. It provides some guidance through parameter descriptions (e.g., 'Change ONLY IF user specifies'), but lacks explicit when/when-not directives or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get explanations of Hacker News terms, culture, and conventions.
| Name | Required | Description | Default |
|---|---|---|---|
| term | Yes | HN term or concept (e.g., "karma", "flagged", "dupe", "Show HN", "Ask HN") |
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 states the tool 'Get explanations,' which implies a read-only operation, but doesn't clarify aspects like response format, potential rate limits, error handling, or whether it requires authentication. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior beyond the basic purpose.
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 a single, efficient sentence: 'Get explanations of Hacker News terms, culture, and conventions.' It is front-loaded with the core purpose, has no unnecessary words, and every part earns its place by specifying the action and scope. This makes it highly concise and well-structured for quick understanding.
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?
Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral traits, output expectations, or integration with sibling tools. Without an output schema, it should ideally hint at what the explanations look like, but it doesn't, leaving some context incomplete for effective agent use.
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 has 100% description coverage, with the 'term' parameter well-documented as 'HN term or concept (e.g., "karma", "flagged", "dupe", "Show HN", "Ask HN")'. The description adds no additional parameter semantics beyond what the schema provides, such as examples or usage tips. Since schema coverage is high, the baseline score of 3 is appropriate, as the schema adequately handles parameter documentation.
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's purpose: 'Get explanations of Hacker News terms, culture, and conventions.' It specifies the action ('Get explanations') and the resource ('Hacker News terms, culture, and conventions'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'user_analysis' or 'search_hn', which might also provide explanatory information in different contexts.
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 by mentioning 'terms, culture, and conventions,' suggesting it's for understanding Hacker News-specific concepts. However, it lacks explicit guidance on when to use this tool versus alternatives like 'search_hn' for general searches or 'user_analysis' for user-related insights. No exclusions or clear alternatives are provided, leaving some ambiguity in context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Hacker News stories and comments. Returns matching content with relevance scores.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search terms (e.g., "rust async", "GPT-4", "startup advice") | |
| tags | No | Filter by: "story" (submissions), "comment" (discussions), "ask_hn", "show_hn", or "poll" | |
| dateRange | No | Time filter: "all" (any time), "last24h", "pastWeek", "pastMonth", or "pastYear" | all |
| sortBy | No | Sort results by relevance score or date posted | relevance |
| limit | No | Default 30, range (1-100). Override ONLY IF user requests. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return type ('matching content with relevance scores'), which is helpful, but fails to address critical aspects like rate limits, authentication needs, error handling, or pagination behavior. For a search tool with 5 parameters, this leaves significant gaps in understanding how the tool behaves in practice.
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 a single, efficient sentence that front-loads the core purpose ('Search Hacker News stories and comments') and immediately states the return value. Every word earns its place with zero redundancy or fluff, 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.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (5 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and return type but lacks behavioral details (e.g., rate limits, errors) and output structure clarification. Without an output schema, the agent must infer the exact format of 'matching content with relevance scores'.
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%, meaning all parameters are well-documented in the schema itself. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain query syntax, tag combinations, or how relevance scores are calculated). Baseline 3 is appropriate when the schema does the heavy lifting.
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 specific action ('Search Hacker News stories and comments') and resource ('Hacker News'), distinguishing it from siblings like 'browse_stories' (likely browsing without search) and 'get_story_details' (fetching specific story details). It explicitly mentions what it returns ('matching content with relevance scores'), making the 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 implies usage for search scenarios but provides no explicit guidance on when to use this tool versus alternatives like 'browse_stories' or 'hn_explain'. It lacks any 'when-not' or prerequisite information, leaving the agent to infer context 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.
Analyze a Hacker News user's profile, karma, and recent submissions.
| Name | Required | Description | Default |
|---|---|---|---|
| username | Yes | HN username (e.g., "pg", "dang", "tptacek") | |
| submissionLimit | No | Default 10, range (0-50). Override ONLY IF user requests. |
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. While it mentions what gets analyzed (profile, karma, submissions), it doesn't describe key behavioral traits such as whether this is a read-only operation, what the output format looks like, potential rate limits, or authentication needs. For a tool with no annotation coverage, this is a significant gap in transparency.
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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence ('Analyze a Hacker News user's profile, karma, and recent submissions') directly contributes to understanding the tool's function, making it appropriately concise and well-structured.
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?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral aspects like output format or operational constraints. Without annotations or an output schema, the description should do more to compensate, but it only meets the minimum viable threshold.
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 schema already fully documents both parameters (username and submissionLimit). The description doesn't add any parameter-specific semantics beyond what's in the schema, such as explaining how submissionLimit affects the analysis or providing examples beyond the schema's descriptions. Baseline 3 is appropriate when the schema does the heavy lifting.
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's purpose: 'Analyze a Hacker News user's profile, karma, and recent submissions.' It specifies the verb ('analyze'), resource ('Hacker News user'), and scope ('profile, karma, and recent submissions'). However, it doesn't explicitly differentiate from sibling tools like 'hn_explain' or 'search_hn' which might also involve user-related analysis, so it falls short of a perfect score.
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 context by specifying what it analyzes (user data), but doesn't provide explicit guidance on when to use this tool versus alternatives like 'search_hn' for broader searches or 'hn_explain' for explanations. It lacks clear when/when-not statements or named alternatives, leaving usage somewhat ambiguous.
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. Dates show when Glama detected each change.
v1.0.0browse_storiesget_story_detailshn_explainsearch_hnuser_analysisEach tool has a clearly distinct purpose: browse_stories for listing stories, get_story_details for fetching story content and comments, hn_explain for term explanations, search_hn for searching content, and user_analysis for user profiles. There is no overlap in functionality, making tool selection unambiguous.
The naming follows a consistent verb_noun pattern with snake_case throughout, such as browse_stories and get_story_details. The only minor deviation is hn_explain, which uses a prefix 'hn_' instead of a verb, but it still fits the overall readable convention.
With 5 tools, the server is well-scoped for interacting with Hacker News. Each tool serves a specific, non-trivial function, such as browsing, fetching details, explaining terms, searching, and analyzing users, making the count appropriate and efficient.
The tool set covers core Hacker News interactions well, including story listing, detail retrieval, searching, and user analysis. A minor gap is the lack of tools for posting or interacting with content, but for a read-focused server, the coverage is comprehensive and allows agents to handle most common tasks.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Browse Hacker News feeds, threads, and user profiles with full-text search.
Deterministic Hacker News developer sentiment, themes & feature requests via MCP. No API key.
HN front-page, Algolia full-text search, and Show HN launch tracker.
Hacker News MCP — search and retrieve stories from Hacker News
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