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AI-native HackerNews MCP Server. Find HN content that matters with explainable quality signals.
AI-native HackerNews MCP Server. Find HN content that matters with explainable quality signals.
Valid MCP server (0 strong, 3 medium validity signals). 5 known CVEs in dependencies (1 critical, 3 high severity) Package registry verified. Imported from the Official MCP Registry.
5 files analyzed · 6 issues found
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-xodn348-readhn": {
"args": [
"readhn"
],
"command": "uvx"
}
}
}From the project's GitHub README.
AI-native HackerNews MCP Server. Find HN content that matters with explainable quality signals.
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%).
# 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:
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.
Ask your AI agent:
The agent calls readhn tools, gets results with quality signals, and explains why each result matters.
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
When you ask HN questions, your AI agent uses these tools:
discover_stories() — Top stories filtered by keywords/score/time, ranked by quality signalssearch() — Algolia search with explainable rankingfind_experts() — Find domain experts using EigenTrust on comment graphexpert_brief() — User profile + activity + trust scorestory_brief() — Story + top comments + signals in one callthread_analysis() — Full comment tree with quality signals per commentEvery response includes signals breakdown: why each result was chosen.
MIT
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