hn-mcp
MCP server enabling AI agents to interact with Hacker News, including fetching full comment trees with depth control, searching stories and comments, and retrieving user profiles.
README
hn-mcp
HN threads have 500+ comments nested 10 levels deep. Your AI agent needs to read them without blowing its context window.
hn-mcp is an MCP server that gives AI agents full access to Hacker News — complete comment trees, search, and user profiles — with depth control so they can explore progressively instead of fetching everything at once.
Features
- Full comment trees — no depth limits, no truncation
- Depth control — fetch just top-level comments or the entire tree
- Smart pruning —
reply_countat cut-off points so agents decide what to expand - Search — full-text search across stories and comments with filters
- No API keys — uses the public Algolia HN API
- 100% test coverage — tested with VCR cassettes, no network calls needed
Typical agent workflow
1. get_thread(42123456, depth=1) → story + 85 top-level comments with reply_counts
2. Agent picks Comment A (47 replies)
3. get_comment_tree(comment_a_id) → full 47-reply subtree
4. Agent summarizes branch, picks next
Or for smaller threads, just get_thread(id, depth=-1) to get the entire tree at once.
Getting started
Standard config works in most tools:
{
"mcpServers": {
"hn": {
"command": "uvx",
"args": ["hn-mcp"]
}
}
}
<details> <summary>Claude Code</summary>
claude mcp add hn -- uvx hn-mcp
Add --scope user to make it available in all projects.
</details>
<details> <summary>Claude Desktop</summary>
Follow the MCP install guide, use the standard config above.
</details>
<details> <summary>Cursor</summary>
Add to your Cursor MCP config (~/.cursor/mcp.json):
{
"mcpServers": {
"hn": {
"command": "uvx",
"args": ["hn-mcp"]
}
}
}
</details>
<details> <summary>Windsurf</summary>
Follow the Windsurf MCP documentation, use the standard config above.
</details>
<details> <summary>VS Code / Copilot</summary>
Add to your VS Code MCP config (.vscode/mcp.json):
{
"mcpServers": {
"hn": {
"command": "uvx",
"args": ["hn-mcp"]
}
}
}
</details>
<details> <summary>From source</summary>
If you want to run from a local clone instead:
claude mcp add hn -- uv run --directory /absolute/path/to/news-ycombinator-mcp hn-mcp
</details>
Prerequisites
- Python 3.12+
- uv (provides
uvx)
Tools
| Tool | Description | Key Inputs | Returns |
|---|---|---|---|
get_thread |
Fetch a story and its comment tree | story_id, depth (0=story only, 1=top-level, N=N levels, -1=full tree) |
Story metadata + pruned comment tree |
get_comment_tree |
Dive into a specific comment's reply subtree | comment_id, depth (default: -1, full subtree) |
Comment + nested replies |
get_stories |
Browse HN by category | category (top, new, ask_hn, show_hn), count |
List of story summaries |
search_stories |
Full-text search for stories | query, sort_by (relevance/date), count, page |
Paginated story results |
search_comments |
Full-text search for comments | query, sort_by, story_id, author, count, page |
Paginated comment results |
get_user |
Fetch a user profile | username |
Username, karma, about, created date |
Depth parameter
The depth parameter on get_thread and get_comment_tree controls how much of the tree you get:
depth=0 (no comments — story metadata only)
depth=1 Comment A (reply_count=3) ← just the comment + count
depth=2 Comment A ← comment + direct replies
├── Reply A1 (reply_count=2)
├── Reply A2 (reply_count=0)
└── Reply A3 (reply_count=1)
depth=-1 Full tree, no pruning
Development
git clone https://github.com/tomwojcik/news-ycombinator-mcp
cd news-ycombinator-mcp
make venv
make install
Run tests
make test
Tests use vcrpy cassettes — no network calls needed. Coverage is reported automatically.
Re-record cassettes
If the Algolia API response format changes:
# In tests/conftest.py, temporarily change record_mode to "new_episodes"
uv run pytest
# Then change it back to "none"
Project structure
src/hn_mcp/
├── app.py # FastMCP instance + tree pruning helpers
├── client.py # HNClient — async Algolia API client
├── server.py # Entrypoint
├── types.py # TypedDict definitions for all responses
└── tools/
├── get_thread.py
├── get_comment_tree.py
├── get_stories.py
├── search_stories.py
├── search_comments.py
└── get_user.py
License
MIT — see LICENSE.
Contributing
Contributions welcome. Please open an issue first to discuss what you'd like to change.
When submitting a PR:
- Add tests for new functionality
- Record VCR cassettes for any new API calls
- Ensure
uv run pytestpasses with 100% coverage
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.