devresearch-mcp

Let Claude Code read developer forums for you.

When you ask "is framework X worth using?" — instead of Claude guessing from stale training data, it searches Hacker News, Reddit, and Lobsters in real time, reads what developers are actually saying, and answers from evidence.

Install

claude mcp add devresearch -- npx -y devresearch-mcp

Requires Node.js ≥ 20. No API key. No config. Restart Claude Code and you're done.

Try it

Ask Claude:

"Use devresearch to check if Bun is production-ready"

Claude will:

1. Search Hacker News, Reddit, and Lobsters for Bun discussions (last 30 days)
2. Pull the top 6 threads with their comment trees
3. Score each post on 6 heuristics — velocity, expert engagement, dissent ratio, buzzword density, comment depth, longevity
4. Summarize: "34 discussions found. Pro camp cites speed and drop-in compatibility; con camp flags native-module edge cases. Expert engagement is high (62%), buzzword density low — substantive discussion, limited hype."

What it can do

| Ask Claude... | Tool used | |---|---| | "What's changed in Tanstack Query v5?" | research — aggregates threads + scores hype | | "Search HN for WebAssembly discussions" | search — keyword search across all platforms | | "What's trending on HN today?" | trending — front page / hot / hottest snapshots | | "Show me HN thread 42" | get_post — deep-dive with full comment tree | | "What's jarredsumner's karma?" | get_user — profile + karma lookup |

You don't need to remember tool names — ask in plain English, Claude picks the right one.

How hype detection works

Each post is scored locally on 6 signals (no LLM calls):

| Signal | What it measures | |---|---| | velocity | Score growth rate per hour since posting | | buzzword_density | Frequency of marketing words ("revolutionary", "10x", "game-changer"...) | | expert_engagement | Share of comments from high-karma authors | | dissent | Share of top-level comments expressing disagreement | | depth | Median comment-tree depth (deeper = more technical debate) | | longevity | How long the discussion stayed active |

A post is classified as:

• strong_hype — high buzzword + high velocity + low expert engagement
• mild_hype — elevated buzzword and velocity with some expert presence
• substantive — low buzzword + high expert engagement
• balanced — signals don't clearly lean either way

Claude then reads the raw data and writes the final narrative — it can cite specific comments, identify key voices, and flag misconceptions that the heuristics alone would miss.

Cache

Results are cached in a local SQLite database at ~/.devresearch-mcp/cache.db with a 24-hour TTL. Re-running the same query within a day is instant and uses no network.

Configuration (optional)

Defaults work out of the box. To customize, create ~/.devresearch-mcp/config.toml:

[sources.reddit]
subreddits = ["programming", "rust", "LocalLLaMA", "ExperiencedDevs"]

[cache]
ttl_hours = 24

[hype_scoring]
buzzwords = ["game changer", "revolutionary", "next-gen", "paradigm shift"]

Override the config path with DEVRESEARCH_CONFIG=/path/to/file.toml.

Limitations

• Reddit uses the public JSON endpoints — subject to rate limits; occasional delays.
• Cross-platform duplicate clustering uses URL normalization + title n-grams. Accurate for canonical links, weaker for text-only discussion threads.
• research returns at most 6 top posts (normal) or 12 (depth: "deep") to keep context usage predictable.
• Heuristic signals are English-biased — buzzword/dissent detection relies on English keyword lists.

License

MIT