io.github.xodn348/readhn
AI-native HackerNews MCP Server. Find HN content that matters with explainable quality signals.
Open source Open in the app JSON README (API)
About
AI-native HackerNews MCP Server. Find HN content that matters with explainable quality signals.
Details
- Kind
- MCP servers
- Topic
- No topic detected
- Publisher
- xodn348
- Origin
- official
- Category
- ferramentas
- Transport
- local
- Version
- 0.2.0
- Stars
- 5
- Forks
- 1
- Open pull requests
- 1
- Last push
- 2026-03-11T00:31:07Z
- Repository state
- ativo
- Language
- Python
- License
- MIT
- Added
- 2026-08-29 04:01:42
- Updated
- 2026-08-29 04:01:42
- Origin id
io.github.xodn348/readhn
README
# readhn <!-- mcp-name: io.github.xodn348/readhn --> [](https://pypi.org/project/readhn/) [](https://github.com/xodn348/readhn) [](https://github.com/xodn348/readhn) [](https://registry.modelcontextprotocol.io/v0.1/servers?search=readhn) AI-native HackerNews MCP Server. Find HN content that matters with explainable quality signals. ## What It Does **Discover** — Filter stories by keywords, scores, time. Get ranked results with quality signals. **Trust** — Find domain experts. See who's talking and why they matter. EigenTrust propagation from seed experts. **Understand** — Every result explains WHY. 5 signals: practitioner depth (30%), thread depth (20%), expert involvement (20%), velocity (15%), references (15%). ## Quick Start ```bash # Install pip install readhn # Auto-configure supported AI agents readhn setup ``` `readhn setup` detects Claude Code, Codex, Cursor, Claude Desktop, Cline, Windsurf, and OpenCode config paths and adds the `readhn` MCP server. Useful setup flags: ```bash readhn setup --list # Show detected agents readhn setup --dry-run # Preview config changes only readhn setup --agents "Cursor" # Configure only specific agents ``` After setup, your AI agent auto-discovers readhn and uses it when you ask HN questions. ### Usage Ask your AI agent: - "Show me top HN stories about Rust this week" - "Find experts who write about databases on HN" - "What did practitioners say about Kubernetes networking?" The agent calls readhn tools, gets results with quality signals, and explains why each result matters. ### Configuration (Optional) ```bash export HN_KEYWORDS="ai,llm,rust,distributed-systems,databases" # Default filter keywords export HN_MIN_SCORE="50" # Minimum story score export HN_EXPERTS="tptacek,simonw,antirez,ept,jepsen" # Seed experts for trust export HN_TIME_HOURS="24" # Time window ``` ## How It Works When you ask HN questions, your AI agent uses these tools: - `discover_stories()` — Top stories filtered by keywords/score/time, ranked by quality signals - `search()` — Algolia search with explainable ranking - `find_experts()` — Find domain experts using EigenTrust on comment graph - `expert_brief()` — User profile + activity + trust score - `story_brief()` — Story + top comments + signals in one call - `thread_analysis()` — Full comment tree with quality signals per comment Every response includes signals breakdown: why each result was chosen. ## License MIT