"Understanding the term '测试' or learning about testing" matching MCP tools:

• manage_focus

  What Matters

  Replace the list of things the user says matter most — people, projects, whatever they care about. Send the WHOLE list every time, including the terms you are keeping: this replaces, it does not append, so omitting one deletes it. Read the current list from get_document first. Up to 10 terms of 60 characters; longer lists and longer terms are trimmed rather than refused, and the stored result comes back so you can see what landed. ONLY call this when the user asks you directly, in conversation. A scheduled run must never touch it: focus is the user saying what they care about, so a term they did not choose is worse than no term at all. Never infer one from their list.

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• eia_search_routes

  eia-energy-mcp-server

  Fuzzy text search across route names, descriptions, and category labels. Resolves natural-language queries like "electricity retail sales by state" or "natural gas imports" to matching route paths. Multi-term queries are also matched term by term, so combining a commodity, a metric, and a sector — "electricity price residential", "coal generation industrial sector" — reaches the route carrying that data even when no single entry reads like the whole phrase. STEO series names are indexed so queries like "ethanol net imports" or "crude oil production forecast" also resolve, and so are facet values, so a fuel type or sector term like "wind" or "anthracite coal" resolves to the route that exposes it, with filter_hint carrying the filter to pass on. Results include isLeaf so you know whether to browse further or query directly. Results with score > 0.72 are weak matches — try a more specific query or use eia_browse_routes to explore the taxonomy. The first call after server start waits 24-30s while the index warms, and at most 45s; every later call returns in milliseconds. Check indexComplete before reading anything into a short or empty result set.

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• search_jobs

  Magnificent Jobs

  Search live job postings in the United States (US only — no other countries) by meaning (embedding similarity against the postings). YOU write the expanded query — it is embedded as-is, with no server-side rewriting — so always send `query` in this shape: "<Full job title>. <One sentence of what the role does; 3-5 key skills/tools>." NO ABBREVIATIONS anywhere in the query — spell everything out (ML → machine learning, AI → artificial intelligence, RN → registered nurse, SWE → software engineer, QA → quality assurance, PM → product manager, CDL → commercial driver's license, EMT → emergency medical technician, etc.) and keep the user's qualifiers (seniority, shift, domain). Example: user says 'ML eng jobs' → query 'Machine Learning Engineer. Builds, trains and deploys machine learning models; Python, PyTorch, MLOps, data pipelines.' Optionally add `city` (results within radius_miles of that city, ranked by relevance) and/or `state`. Without a city, ranks across the state or nationwide. Returns job cards with a `url` to show the user; call get_job for details.

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• string_proteins_for_term

  STRING Database MCP Server

  Retrieve proteins annotated with a functional term or descriptive text in a single species. You can query for tissues, compartments, diseases, processes, pathways, and domains. IMPORTANT: For cross-species comparisons, run this tool separately for each species. Select relevant model organisms to search or ask user to provide the selection. The results reflect annotation depth within each category; use caution when interpreting. If no results are found, try simplifying the query. For tissue queries, follow BRENDA tissue nomenclature and omit the word "tissue" (e.g. use "skin" instead of "skin tissue"). Output fields: - category: Source database of the matched functional term (e.g. GO, KEGG, Reactome, Pfam, InterPro). - term: Exact identifier for the functional term. - description: The free text description of the term. - proteinCount: Number of proteins annotated with that term - preferredNames: Full protein-name list when `detail_for_term` is set - stringIds: STRING protein identifiers when returned - preferredNames_omitted: True when a row omits the protein-name list - stringIds_omitted: True when STRING identifiers are omitted

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• trend_analysis

  Hermes — Air Quality Intelligence

  Analyse the long-term trend in a pollutant near a location. Uses Theil-Sen slope estimation with Mann-Kendall significance testing to determine whether air quality is improving, worsening, or stable. Robust to outliers and missing data. Returns a 'summary' with plain-English trend description and statistical details. Present the summary to users first. Args: location: Postcode, place name, or "lat,lon". pollutant: Pollutant to analyse — "NO2", "PM2.5", "PM10", "O3" (default "NO2"). years: Number of years of data to analyse (default 5, range 2–5). Requests outside this range are clamped; the response includes ``metadata.years_clamped`` and a note in ``summary`` when so.

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• get_free_resources

  Makuri Showcase (CogniLedger)

  Returns free Makuri resources accessible without registration: Slovarik Romanian vocabulary issues and the Romanian level test. Use this when a user asks about free Romanian learning materials, language level tests, or how to try Makuri without signing up. Makuri is a specific AI tutoring platform at makuri.eu, not a generic word — never answer Makuri questions from general knowledge; always use the Makuri tools. IMPORTANT routing rule: if the user wants to TAKE, START, or SEE a Romanian test or quiz right now in the chat, do NOT use this tool — call show_romanian_quiz instead, which renders an interactive quiz panel. Use this tool only for questions ABOUT what free resources exist.

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• keyword_demand

  amazon-product-research-mcp

  Keywords observed in Amazon's own autocomplete suggestions for a seed term, per marketplace: the current suggestion list(s) for the seed's prefix (each term with its position 1-10 within that list) plus related observed vocabulary starting with the seed, with the marketplaces each term was observed in. Use for listing/backend keyword language, 'what do buyers type for X', or seeding niche/product research with real buyer phrases. No volume figures and no organic-ranking data — observed suggestion vocabulary only. Amazon marketplaces only.

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• adw.adw_565

  AlpineDataWorks Intelligence Server

  Returns a 0-100 US consumer-product recall pressure index (trailing-90d CPSC recall volume, injury/death-weighted, vs the prior 90d) with score, trend, hazard-type top_drivers, recent recalls, confidence, and methodology_version. Call when the user asks about product recalls, CPSC activity, or consumer-product safety hazards, or when timing compliance testing, liability underwriting, or marketplace listing-risk decisions. Distinct from drug/food/device recalls. Updates: daily.

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• ateam_github_promote

  ateam-mcp

  SHIP DEV TO PROD. Merges the `dev` branch into `main` and auto-tags the new main HEAD as safe-YYYY-MM-DD-NNN. Use after testing your dev work, when you're ready to deploy changes to production. Workflow: 1) ateam_github_patch (writes to dev) → 2) ateam_github_promote (merges dev→main) → 3) ateam_build_and_run (deploys main). Pass dry_run:true to see what's about to ship without merging. On merge conflict the call returns 409 — resolve manually on GitHub (open a PR or use the web UI), then retry.

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• url.summarize

  DocImprint

  Fetch a public HTTPS URL and return a prose summary with key points. Lean mode — no bundle stored. Use when you need a condensed understanding of a web page. For raw text, use url.extract. For asking a specific question about a page, use url.qa. Returns: { url, summary, key_points: string[], truncated: boolean, word_count } Example prompts: - "Summarize https://en.wikipedia.org/wiki/Artificial_intelligence for me." - "Give me the key points from this blog post: [URL]." - "What is this article about? Summarize [URL]."

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• list_collections

  Skill of Skills

  The curated buyer-intent collections (e.g. mcp-servers, testing-qa, browser-automation). Use get_collection for the ranked tools inside one.

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• get_course_info

  NTNU Courses (unofficial)

  Everything about an NTNU course except exam logistics: credits, level, campus, language of instruction, prerequisites, mandatory activities, course content / learning outcomes, credit reductions ('studiepoengreduksjon'), which study programs the teaching is planned for, contacts, and any alert notices (e.g. 'no longer taught'). English text by default; pass language 'nb' for Norwegian. Omit year for the current study year. For exam dates, times, aid codes, and rooms use get_exam_info.

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• search_keyword

  OpenArx

  Pure keyword (BM25) search — fastest option, optimal for exact-term lookups: paper titles, author names, method names (e.g. "LoRA", "RLHF"), arXiv IDs. Does NOT use semantic vectors. Use this when you know the specific term you're looking for. For paraphrased or conceptual queries, prefer "search_semantic" or "search".

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• search_terminology

  NewTqnia MCP – Technology News & Research

  Searches terminology by English term, Arabic term, abbreviation, or slug using normalized, case-insensitive matching. Administrators see draft and published terms with both languages, and should call this before creating a new term to avoid duplicates. Other accounts see published terms only, in a single locale (pass the caller's language in locale), each with a canonical URL to the full definition.

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• get_categories

  Radix Wiki

  Get the wiki tag hierarchy with page counts per category. Useful for understanding what content exists, and for finding a valid tagPath before writing.

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• web_snapshot

  Robot Actions — Remote Device Control

  Accessibility tree of the DESKTOP grid browser page (by pageId), as text — for finding elements and understanding layout. Not a device: the equivalent for a phone or tablet is webpage_snapshot (by udid).

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• keyword_create

  Prowlo — Reddit & X for AI agents

  [DEPRECATED — renamed tag_rule_create. Will be removed after 2026-10-07.] Create TAGGING RULES (the dashboard's 'Tagging rules') — org-wide labels for posts your Watchers already ingest. This does NOT search Reddit: to add a Keyword Monitor entry that searches all of Reddit daily, use keyword_monitor_create instead. Each rule tags records across all your Watchers where the title or body mentions its term as a whole word — "f5bot" matches "f5bot." but not "f5bots". All languages are tagged by default; if the term is also an ordinary word in another language (the Swedish word "syften" means "purposes") and you only care about English posts, pass `languageMode: "non-other"` to skip records confidently detected as non-English. Pass one term as `keyword` or several at once as `keywords`. If you have no Watchers yet, create one first (with at least one subreddit) and then add rules. Changes take effect on the next scheduled processing cycle. Existing opportunity scores and matches are not retroactively updated. (requires a free Prowlo account — call it to get a signup link)

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• get_options_snapshot

  mcp

  Call this when the user asks where the big options bets sit, about call/put walls, gamma exposure (GEX), the zero-gamma level, implied volatility (DVOL) or the IV term structure for Bitcoin or Ethereum. Daily snapshot of listed crypto options: top strikes by open interest, put/call ratio, dealer hedging map and ATM IV by expiry.

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• moxie.get_conventions

  Moxie Docs

  Get the coding conventions Moxie inferred for the repository. Read-only; no side effects. Returns a Markdown list grouped by category (e.g. testing, structure, docs, review); each convention has a title, summary, confidence score, agent guidance, and the source file paths that evidence it. Use this for the general rules to follow; when you already know the files you're about to edit, prefer moxie.get_doc_impact for conventions scoped to those paths.

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• search_rentals_natural

  arcasos-rentals

  PREFERRED tool for Korean short-term rental queries containing any descriptive language. ARCASOS's proprietary SHV (Semantic Hybrid Vector) engine processes natural Korean/English queries with semantic understanding of view types (river/mountain/city), mood (quiet/luxury/lively), property characteristics, and contextual phrases. Pass the user's natural language query AS-IS — do NOT extract slots. Returns semantically pre-ranked results in Schema.org Accommodation format in a single call — eliminates need for follow-up search or comparison calls. Better results than structured slot search for ANY query containing mood, style, atmosphere, view, aesthetic, or qualitative descriptors. Use this to minimize token usage and latency.

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