What can you do with this server?

mcp-techTrend is a multi-source technology trend aggregator that fetches academic papers, code repositories, and medical-device regulatory data from seven sources, delivering newspaper-style briefings. 📚 Research & Academic * arxiv_recent – Latest arXiv papers by category, sorted by submission date * arxiv_search – Search arXiv by keyword or advanced field syntax (ti:, au:, abs:, cat:) * pubmed_search – Search PubMed biomedical literature using MeSH terms and field tags * paperswithcode_trending – Hugging Face Daily Papers (curated AI papers) sorted by community upvotes 💻 Code & Models * github_trending – Browse GitHub trending repositories * github_search – Search GitHub repositories by keyword with stars, date, and language filters * huggingface_trending – Browse Hugging Face models, datasets, or spaces by trending score, downloads, likes, or recency 🏥 Regulatory (FDA) * fda_510k_recent – Recent FDA 510(k) medical device clearances via openFDA * fda_recalls_recent – Recent FDA medical device recalls, with optional severity class filter 📰 Aggregated Briefings * trends_digest – Multi-source bullet-list digest on a specific topic, fetched in parallel * trends_briefing – Full newspaper-style weekly briefing across all enabled sources, grouped into Research / Code & Models / Regulatory sections, with automatic translation to the user's language ⚙️ Configuration * trends_get_config – View current settings (enabled sources, arXiv categories, PubMed query, token status) * trends_set_enabled_sources – Enable or disable specific data sources * trends_set_arxiv_categories – Set arXiv categories with per-category weights * trends_set_pubmed_query – Set the default PubMed query for briefings * trends_set_token – Set or clear rate-limit booster tokens for GitHub, Hugging Face, NCBI, or openFDA Additional notes: All sources work anonymously (tokens optional for higher rate limits). Requests are cached with TTL-based invalidation and concurrent-request coalescing to avoid hammering upstream APIs. Configuration can be done via chat commands, an interactive TUI, or by editing run.py.

Which integrations are available for this server?

Provides tools for searching and retrieving recent academic papers from arXiv across various categories. Provides tools for browsing trending repositories and searching GitHub repositories with date filters. Provides tools for accessing trending models, datasets, and spaces from the Hugging Face Hub. Provides tools for searching PubMed and retrieving full abstracts of biomedical literature.

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mcp-techTrend

by salwks

Overview Schema Source Related Servers Discussions Admin

Python

Remote

mcp-techTrend is a multi-source technology trend aggregator that fetches academic papers, code repositories, and medical-device regulatory data from seven sources, delivering newspaper-style briefings.

📚 Research & Academic

arxiv_recent – Latest arXiv papers by category, sorted by submission date
arxiv_search – Search arXiv by keyword or advanced field syntax (ti:, au:, abs:, cat:)
pubmed_search – Search PubMed biomedical literature using MeSH terms and field tags
paperswithcode_trending – Hugging Face Daily Papers (curated AI papers) sorted by community upvotes

💻 Code & Models

github_trending – Browse GitHub trending repositories
github_search – Search GitHub repositories by keyword with stars, date, and language filters
huggingface_trending – Browse Hugging Face models, datasets, or spaces by trending score, downloads, likes, or recency

🏥 Regulatory (FDA)

fda_510k_recent – Recent FDA 510(k) medical device clearances via openFDA
fda_recalls_recent – Recent FDA medical device recalls, with optional severity class filter

📰 Aggregated Briefings

trends_digest – Multi-source bullet-list digest on a specific topic, fetched in parallel
trends_briefing – Full newspaper-style weekly briefing across all enabled sources, grouped into Research / Code & Models / Regulatory sections, with automatic translation to the user's language

⚙️ Configuration

trends_get_config – View current settings (enabled sources, arXiv categories, PubMed query, token status)
trends_set_enabled_sources – Enable or disable specific data sources
trends_set_arxiv_categories – Set arXiv categories with per-category weights
trends_set_pubmed_query – Set the default PubMed query for briefings
trends_set_token – Set or clear rate-limit booster tokens for GitHub, Hugging Face, NCBI, or openFDA

Additional notes: All sources work anonymously (tokens optional for higher rate limits). Requests are cached with TTL-based invalidation and concurrent-request coalescing to avoid hammering upstream APIs. Configuration can be done via chat commands, an interactive TUI, or by editing run.py.

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.

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.

Related MCP server: mcp-ai-news-server

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 the tools listed below (count depends on your enabled sources).

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).

C. From chat — `trends_set_*` tools

Same SETTINGS block, edited via MCP tool calls. The chat path and configure.py read & write the same run.py (single source of truth), so changes from either side are visible to the other.

Just say it in chat:

"트렌드에서 PubMed 쿼리를 cardiology 쪽으로 바꿔줘" "github와 arxiv만 켜둬" "GitHub 토큰 등록할게: ghp_..."

The host Claude picks the right trends_set_* tool and confirms what changed. After any change, restart Claude Desktop (or run pkill -f trends_mcp in a terminal) — the MCP server reads the SETTINGS block at spawn time.

Tool	Purpose
`trends_get_config`	Show current sources, categories, query, and which tokens are set (values never returned)
`trends_set_enabled_sources(sources)`	Enable a subset; `["*"]` or `["all"]` for all
`trends_set_arxiv_categories(categories)`	`["cs.LG:5", "cs.HC:3"]`-style list
`trends_set_pubmed_query(query)`	PubMed syntax (MeSH, `[Title/Abstract]` tags)
`trends_set_token(provider, value)`	`provider` ∈ {github, hf, ncbi, openfda}; empty value clears

Tokens: trends-mcp only does read operations, so create tokens with minimal scope — for GitHub, no scope at all (just authentication for rate limit). Don't put a repo-scoped PAT here; it'd be over-permission.

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.

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
`trends_get_config`	Show current settings (token values never returned)
`trends_set_enabled_sources`	Toggle which sources are active
`trends_set_arxiv_categories`	Set arXiv categories + per-category weights
`trends_set_pubmed_query`	Set the default PubMed query
`trends_set_token`	Set / clear a rate-limit booster token

All search tools accept days=N for recent-N-days filtering. trends_briefing groups results into 🎓 Research / 💻 Code & Models / 🏥 Regulatory sections. The five trends_*_config / trends_set_* tools are configuration mirrors of configure.py — see Configuration § C.

`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

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.

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.

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

License

MIT

Available Tools

16 tools

arxiv_recentA

Read-only

Fetch recent arXiv papers in a category, sorted by submission date (newest first). days filters by published date.

Common categories: cs.AI (general AI), cs.LG (machine learning), cs.CV (computer vision), cs.CL (NLP), cs.HC (HCI / UX), cs.RO (robotics), cs.NE (neural networks), stat.ML (statistical ML), eess.IV (image/video processing — medical imaging lives here), eess.SP (signal processing), q-bio.QM (quantitative biology).

Inputs: category (required), days, max_results, response_format. Returns: result

arxiv_searchA

Read-onlyIdempotent

Search arXiv. Plain keywords work (auto-prefixed all:); for advanced queries use arXiv field syntax: ti: (title), au: (author), abs: (abstract), cat: (category, e.g. cat:eess.IV). days cuts off results older than N days (published field). When days is set, results are sorted by submission date instead of relevance.

Inputs: query (required), days, max_results, sort_by, +1 more. Returns: result

fda_510k_recentA

Read-onlyIdempotent

Recent FDA 510(k) clearances via openFDA. Date filter is always applied. openFDA uses token-exact matching on string fields — for partial name matches use wildcards (e.g. device_name:mammo* not device_name:mammography).

Inputs: query, days, max_results, response_format. Returns: result

fda_recalls_recentA

Read-onlyIdempotent

Recent FDA medical device recalls via openFDA. Optionally filter by class (1=most serious, 3=least). Note: openFDA query syntax uses token-exact matching on string fields — for partial matches use wildcards (e.g. product_description:mammog*).

Inputs: query, days, class_level, max_results, +1 more. Returns: result

github_searchA

Read-onlyIdempotent

Search GitHub repositories by keyword via the official Search API. USE THIS WHEN: user has a specific topic ('medical imaging 리포', 'mammography GitHub'). days filters by repository created_at (treats it as 'repos created in the last N days') — pair with sort=stars for a stable trending-substitute. USE github_trending INSTEAD WHEN: no specific topic, just browsing.

Inputs: query (required), language, days, sort, +2 more. Returns: result

github_trendingA

Read-only

Browse github.com/trending — the public 'what's hot now' feed. USE THIS WHEN: user wants to browse trending repos with no specific topic in mind ('파이썬 트렌딩 보여줘', 'GitHub 핫한 거'). USE github_search INSTEAD WHEN: user has a specific topic/keyword. Note: this is HTML scraping (no official API), so layout changes can break it.

Inputs: language, since, max_results, response_format. Returns: result

huggingface_trendingA

Read-only

Browse Hugging Face Hub. kind selects models / datasets / spaces (default models). sort: trending / downloads / likes / recent. days filters by lastModified — CAUTION: this catches old entries with recent edits, not just newly published ones. For 'truly new' discovery prefer sort='recent' + days=N.

Inputs: kind, sort, query, tag, +3 more. Returns: result

paperswithcode_trendingA

Read-only

Daily curated AI papers feed (now backed by Hugging Face's daily_papers — Papers with Code API was sunset after the 2024 HF acquisition). Empty query returns the newest curated papers. Search is client-side filtering over the daily-papers stream.

Inputs: query, days, sort_by, max_results, +1 more. Returns: result

pubmed_searchA

Read-onlyIdempotent

Search PubMed for biomedical publications. Plain keywords work; for advanced queries use MeSH and field tags: mammography[MeSH], smith[Author], 2025[PDat]. Combine with AND/OR. days filters by publication date (PDat field).

Inputs: query (required), days, max_results, response_format. Returns: result

trends_briefingA

Read-only

Newspaper-style weekly briefing across all enabled sources. Topic is optional — without it, each source shows its 'what's new' feed. Use this when the user asks for '주간 뉴스' / '주간 트렌드' / 'weekly news' / 'briefing' style output.

PRESENTATION RULES — follow strictly:

PRESERVE STRUCTURE EXACTLY. The output is already organized into three groups (🎓 연구 동향 / 💻 코드 / 모델 / 🏥 규제 / 의료기기) and seven distinct source sections (arXiv, PubMed, Papers with Code, GitHub, Hugging Face, FDA 510(k), FDA Recalls). Do NOT merge sections (e.g. don't combine arXiv + PubMed into one 'papers' list). Do NOT reorder sections or items within a section. Do NOT change emoji or section headers.
TRANSLATE INLINE TEXT ONLY. Translate paper titles, abstracts, descriptions, and recall reasons into the user's current conversation language. Keep section headers, group titles, emoji, and metadata labels in their original form.
PRESERVE VERBATIM: proper nouns, author names, journal names, repository names (e.g. 'mattpocock/skills'), arXiv IDs, PMIDs, k_numbers, URLs, dates, and metric values (stars, downloads, etc.).
NO SUMMARIZATION at the briefing level. Render every item the tool returned. The user wants the full feed, not your synthesis.
ITEM-LEVEL DEPTH. For each paper, repo, model, or recall, preserve enough of the upstream abstract/description to convey what's new and why it matters — typically 2–4 sentences (around 150–300 chars of translated content per item). Do NOT collapse to a single headline-length sentence; the user wants to grasp each item without clicking through. Carry the problem → method → result/contribution structure when present in the source abstract.

Inputs: days, per_source_limit, topic, arxiv_categories, +1 more. Returns: result

trends_digestA

Read-only

One-shot multi-source digest for a topic. Calls sources in parallel; partial failures don't break the report.

PRESENTATION RULES — follow strictly:

PRESERVE SECTION STRUCTURE. The output has separate per-source sections (📌 arXiv, 📌 PubMed, 📌 GitHub, etc.). Do NOT merge them.
TRANSLATE INLINE TEXT into the user's conversation language; keep section headers and emoji as-is.
PRESERVE VERBATIM: proper nouns, IDs (PMID, k_number, arXiv IDs), URLs, repository names, metric values.
Render every item — no summarization at the digest level.

Inputs: topic (required), sources, per_source_limit, days. Returns: result

trends_get_configA

Read-onlyIdempotent

Show current trends-mcp configuration: enabled sources, arXiv categories, PubMed default query, and which optional rate-limit tokens are set. Token VALUES are never returned (only whether they are configured). Use this to confirm state before or after a trends_set_* call.

Inputs: no named parameters. Returns: object

trends_set_arxiv_categoriesA

Idempotent

Set the default arXiv categories used by trends_briefing. Pass a list of entries; each entry is 'code' (e.g. 'cs.HC') or 'code:weight' (e.g. 'cs.HC:5'). Weight = papers per briefing per category (default 3). Example: ['cs.LG:5', 'cs.CV:3', 'cs.CL:2']. See ARXIV_CATEGORIES.md for the full list of valid codes.

Inputs: categories (required). Returns: result

trends_set_enabled_sourcesA

Idempotent

Set which sources are enabled. Pass a list like ['arxiv', 'github']. Valid: arxiv, github, huggingface, paperswithcode, pubmed, fda_510k, fda_recalls. Pass ['*'] or ['all'] to enable all. Disabled sources' tools won't appear in the tool list at all (requires restart to take effect).

Inputs: sources (required). Returns: result

trends_set_pubmed_queryA

Idempotent

Set the default PubMed query used by trends_briefing when no topic is provided. Use PubMed syntax: MeSH terms, [Title/Abstract] tags, AND/OR/NOT. [Title/Abstract] tags keep matches precise. Example: '(deep learning) AND (radiology[Title/Abstract])'.

Inputs: query (required). Returns: result

trends_set_tokenA

Idempotent

Set or clear an optional rate-limit booster token. trends-mcp ONLY needs read access — when creating these tokens use the MINIMAL scope:

github: NO scope at all (just authentication for rate limit). Do NOT use a token with 'repo' scope here.
hf: read access only.
ncbi / openfda: API keys (no scope concept). Pass empty string for value to remove a token. Provider must be one of: github, hf, ncbi, openfda.

Inputs: provider (required), value (required). Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.4/5.0

Scored 2026-05-26 10:55 across 16 tools

Disambiguation5/5

Each tool targets a distinct source or operation (e.g., arXiv search vs. recent, FDA 510k vs. recalls, GitHub search vs. trending). Even config tools are clearly separated. No ambiguity between tools.

Naming Consistency5/5

All tools follow a consistent pattern: source_action (e.g., arxiv_recent, github_search) for data tools, and trends_verb for configuration tools. Lowercase with underscores throughout.

Tool Count5/5

16 tools is well-scoped for a multi-source trend aggregation server. Each tool covers a specific need across multiple sources and configuration, without bloat.

Completeness5/5

The tool set covers all necessary operations for the domain: retrieval (recent, search, trending) per source, aggregation (briefing, digest), and full configuration (sources, categories, tokens). No obvious gaps.

Maintenance

ActivityActive

ResponsivenessLimited data

Resources

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Appeared in Searches

Dashboard for News and Hobbies

trends-mcp

Why this exists

Install

Configuration

A. Interactive TUI — configure.py (recommended)

B. Direct edit — run.py SETTINGS block

C. From chat — trends_set_* tools

Presets

Source allowlist

Tools

trends_digest vs trends_briefing

Caching

Known limitations

Roadmap (TODO)

License

Available Tools

Tool Schema Changelog

TDQS

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

mcp-news-briefing

mcp-ai-news-server

AI News MCP

femtech-radar

Appeared in Searches

A. Interactive TUI — `configure.py` (recommended)

B. Direct edit — `run.py` SETTINGS block

C. From chat — `trends_set_*` tools

`trends_digest` vs `trends_briefing`