trends-mcp

한국어 문서: README.ko.md

A single MCP server that pulls academic + code + medical-device-regulatory trend data from seven sources and renders newspaper-style briefings — with per-domain tuning baked in.

Source	Tools	Notes
arXiv	arxiv_recent, arxiv_search	Per-category round-robin so small categories aren't drowned by big ones
PubMed	pubmed_search	Full abstracts via efetch.fcgi
HF Daily Papers	paperswithcode_trending	Sorted by community upvotes (replaces sunset PwC API)
GitHub	github_trending, github_search	Trending page scrape + Search API with created:> date filter
Hugging Face	huggingface_trending	Models / datasets / spaces, trending or recent
openFDA 510(k)	fda_510k_recent	Device clearances
openFDA Recalls	fda_recalls_recent	Recall events with class filter
(aggregators)	trends_digest, trends_briefing	Multi-source parallel calls

trends_briefing is the headline tool: invoke "weekly news" / "주간 뉴스" and get a newspaper-formatted briefing across all enabled sources, automatically translated into the user's conversation language by the LLM.

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Why this exists

Most academic / code / regulatory MCP servers are single-source. This one is multi-source and domain-aware: a researcher tracking medical-imaging AI, an ML engineer following ML papers, a security analyst watching CVEs and trending repos — all configure once via python configure.py, then trends_briefing becomes the "Monday morning newspaper" for their domain.

What makes it useful:

• Newspaper format with translation hint — the LLM auto-translates source text (paper abstracts, recall reasons, etc.) to your conversation language while preserving identifiers, URLs, and metric values verbatim.
• Per-category round-robin for arXiv — cs.HC (~50 papers/wk) doesn't get drowned by cs.LG (~1500/wk) when both are tracked together.
• TTL cache + concurrent-request coalescing — repeat calls and parallel briefings don't hammer upstream APIs.
• No required tokens. All seven sources work anonymously; tokens just raise the per-source rate limit ceiling.
• Sandbox-safe Python launcher. Bypasses the claude_desktop_config.json env block (which truncates whitespace-containing values on some macOS builds) by setting environment variables in Python before handing off to the server.

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Install

git clone https://github.com/salwks/mcp-techTrend.git
cd mcp-techTrend
python3 -m venv .venv
.venv/bin/pip install -r requirements.txt

Connect to Claude Desktop by editing ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "trends": {
      "command": "/path/to/trends-mcp/.venv/bin/python",
      "args": ["/path/to/trends-mcp/run.py"]
    }
  }
}

⚠️ args points at run.py (the launcher), not trends_mcp.py. The launcher sets domain-specific env vars before the server starts.

Restart Claude Desktop. The trends server should appear with 11 tools.

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Configuration

One source of truth: run.py. Two ways to edit it:

A. Interactive TUI — configure.py (recommended)

python configure.py

═══ trends-mcp 설정 ═══
  [1] Active sources       (7/7 enabled)
  [2] arXiv categories     (4 entries · 13 papers/wk)
  [3] PubMed query
  [4] API tokens           (0/4 set)
  [5] Show current config
  [6] Save and restart
  [7] Quit without saving

Toggle sources with numbers, set arXiv weights with set 1 7, apply presets with preset medical-imaging, save with [6]. The save action backs up to run.py.bak, writes the new SETTINGS block (AST-based — never touches non-config code), and runs pkill -f trends_mcp so Claude Desktop respawns the server with the new config on next call.

The TUI menu labels are in Korean; commands and presets are in English. i18n of the TUI itself is on the v0.2 roadmap.

Single-shot modes:

python configure.py --show       # print current config
python configure.py --restart    # pkill stale MCP processes

B. Direct edit — run.py SETTINGS block

TRENDS_ENABLED_SOURCES = ""                          # "" = all
TRENDS_ARXIV_CATEGORIES = "cs.LG:5,cs.CV:3,cs.CL:3,cs.AI:2"
TRENDS_DEFAULT_PUBMED_QUERY = "(deep learning OR AI) AND (medical OR clinical)"
# GITHUB_TOKEN = "ghp_..."         # raises 60 → 5,000 req/h
# HF_TOKEN = "hf_..."
# NCBI_API_KEY = "..."             # raises 3 → 10 req/s for PubMed
# OPENFDA_API_KEY = "..."          # raises 240 → 120,000 req/day

Restart Claude Desktop after saving (or pkill -f trends_mcp).

Presets

# AI/ML researcher (default)
TRENDS_ARXIV_CATEGORIES = "cs.LG:5,cs.CV:3,cs.CL:3,cs.AI:2"

# Medical imaging / clinical AI
TRENDS_ARXIV_CATEGORIES = "eess.IV:5,cs.CV:3,cs.HC:2,q-bio.QM:2"

# Robotics
TRENDS_ARXIV_CATEGORIES = "cs.RO:5,cs.AI:3,cs.LG:2,cs.CV:2"

# HCI / UX
TRENDS_ARXIV_CATEGORIES = "cs.HC:5,cs.CY:3,cs.AI:2,cs.SI:2"

# Security
TRENDS_ARXIV_CATEGORIES = "cs.CR:5,cs.LG:2,cs.NI:2"

# Computational biology
TRENDS_ARXIV_CATEGORIES = "q-bio.QM:4,q-bio.GN:3,q-bio.BM:3,stat.AP:2"

Common arXiv categories (full reference: ARXIV_CATEGORIES.md):

Code	Field	Weekly papers (approx)
cs.AI	Artificial Intelligence	500–800
cs.LG	Machine Learning	1,500–2,000 (largest)
cs.CV	Computer Vision	1,000–1,500
cs.CL	NLP	500–800
cs.HC	HCI / UX	50–100
cs.RO	Robotics	100–200
cs.CR	Security	~200
eess.IV	Image/Video Processing (medical imaging)	100–200
q-bio.QM	Quantitative biology	50–100

Source allowlist

TRENDS_ENABLED_SOURCES = "arxiv,github,huggingface,paperswithcode"
# → fda_510k, fda_recalls, pubmed tools won't appear in the tool list at all

Empty / "*" / "all" = enable everything. Disabled sources don't register their tools, so the chat tool list itself shrinks. trends_digest and trends_briefing remain registered and skip disabled sources gracefully.

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Tools

Tool	Purpose
arxiv_recent	Recent papers in one category, by submission date
arxiv_search	Keyword / field-syntax search (ti:, au:, abs:, cat:)
pubmed_search	PubMed search (MeSH terms, field tags) — abstracts via efetch
paperswithcode_trending	HF Daily Papers, sorted by community upvotes
github_trending	Browse github.com/trending (HTML scrape)
github_search	GitHub Search API; days filters by created:
huggingface_trending	HF Hub models / datasets / spaces
fda_510k_recent	Recent FDA 510(k) clearances
fda_recalls_recent	Recent FDA medical-device recalls (class filter)
trends_digest	Multi-source bullet-list digest, given a topic
trends_briefing	Multi-source newspaper briefing; topic optional

All search tools accept days=N for recent-N-days filtering. trends_briefing groups results into 🎓 Research / 💻 Code & Models / 🏥 Regulatory sections.

trends_digest vs trends_briefing

	trends_digest	trends_briefing
Topic	required	optional ("what's new" mode)
Source range	configurable subset (default 4)	all enabled sources
Format	bullet-list digest	grouped newspaper format
Use case	topic deep-dive	regular weekly briefing

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Caching

Per-process in-memory TTL cache wraps every HTTP response. Concurrent identical requests are coalesced via per-key asyncio.Lock — N parallel callers fire one upstream request.

TTL group	Length	Tools
Trending	5 min	github_trending, paperswithcode_trending, huggingface_trending (trending sort), github_search (with days)
Default	10 min	arxiv_recent, arxiv_search, github_search, huggingface_trending (other sorts)
Static	1 hour	pubmed_search, fda_510k_recent, fda_recalls_recent

Up to 256 entries; oldest evicted when full. No way to disable — TTLs are short enough that staleness is bounded.

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Known limitations

• GitHub Trending is HTML scraping — no official API exists. Layout changes can break it. Stable trending substitute: github_search with days=7 and sort=stars.
• HF trendingScore is undocumented. API surface may change.
• HF Daily Papers covers ~50 curated papers/day, not all of arXiv. It's a "what was talked about" feed, not exhaustive.
• arXiv has no native trending — we approximate via category-balanced recent-submissions feeds.
• openFDA classification field sometimes returns None even on recently classified recalls (upstream data lag). Search index lags too.

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Roadmap (TODO)

• v0.2: i18n for the TUI menu and briefing section headers
• bioRxiv / medRxiv via RSS
• Semantic Scholar (citation graph)
• openFDA Adverse Events (MAUDE)
• EU EUDAMED scraping
• PMDA (Japan medical devices)
• MFDS (Korea medical devices)
• Mock-based test suite for CI

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License

MIT