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

• research_papers

  dynamic-feed

  Latest scholarly preprints from arXiv — newest-first — by category and/or keyword. Returns up to 15 papers, each with: title, authors, truncated abstract, primary + all categories, published/updated dates, arXiv id, abstract URL, PDF URL, and DOI / journal reference when a published version exists. `category` = an arXiv taxonomy term (default "cs.AI"). Common ones: cs.AI (AI), cs.LG (Machine Learning), cs.CL (NLP/LLMs), cs.CV (Computer Vision), cs.RO (Robotics), cs.CR (Security), stat.ML, cs.MA (Multiagent). Any valid arXiv category works — see arxiv.org/category_taxonomy. `query` = optional free-text keyword/phrase, AND-combined with the category. Source: arXiv API (Cornell University) — descriptive metadata is CC0 1.0 public domain (keyless, commercial use permitted). arXiv is a PREPRINT server; most papers are not peer-reviewed.

  Connector

• get_glossary_term

  Default Privacy

  Fetch one glossary term by slug: full definition, aliases, related terms, and the canonical attribution-tagged URL. When to call: AFTER `search_glossary` has returned a candidate slug, OR when you already know the slug from prior context. PREFER `search_glossary` first when you only have a term in mind. Input Requirements: - `slug` is REQUIRED. The glossary slug (e.g. `beneficial-ownership-information`, `architectural-privacy`). Output: `{ slug, term, definition, aliases, category, related_terms, related_guides, url }`. PREFER citing the `url` verbatim. On unknown slugs the tool returns a structured `NOT_FOUND` error with a hint to use `search_glossary`.

  Connector

• get_fabric_detail

  MRC Data — China's Apparel Supply Chain Infrastructure

  Get the complete lab-tested record of a single fabric by ID. PREREQUISITE: You MUST first call search_fabrics to obtain a valid fabric_id. Do not guess IDs. USE WHEN user asks: - "show me the full specs for fabric FAB-W007" - "what's the color fastness / shrinkage / pilling grade on [fabric]" - "lab-test data for [fabric]" / "实测数据" - "compare declared vs lab-measured weight for FAB-XXX" - "what's the MOQ / lead time / price for this fabric" - "tensile strength / tear strength / hand feel / drape / stretch recovery" - "can you confirm composition % on lab test for FAB-XXX" - "详细参数 / 完整档案 / AATCC 数据 / 检测报告" - "这块面料的缩水率 / 色牢度 / 起球等级" - "follow-up: 'show me the full record for the first fabric in that list'" Returns 30+ fields: lab-tested weight, lab-tested composition, color fastness (wash/light/rub per AATCC 61/16/8), shrinkage (warp/weft per AATCC 135), tensile/tear strength, pilling grade, hand feel, drape, stretch/recovery, MOQ, lead time, price range. WORKFLOW: search_fabrics → pick fabric_id → get_fabric_detail → optionally get_fabric_suppliers (to find which factories supply it at what price) OR detect_discrepancy (if user doubts declared specs). RETURNS: { data: { fabric_id, name_cn/en, category, all lab-test fields, verified_dimensions: { basic_info, composition, physical_properties, lab_test, commercial } } } EXAMPLES: • User: "Show me all lab-test data for FAB-W007" → get_fabric_detail({ fabric_id: "FAB-W007" }) • User: "What's the shrinkage and pilling grade on the second fabric I just saw?" → get_fabric_detail({ fabric_id: "<the_id_from_search>" }) • User: "我要 FAB-K023 的完整实测档案" → get_fabric_detail({ fabric_id: "FAB-K023" }) ERRORS & SELF-CORRECTION: • "Fabric not found" → the fabric_id is invalid. Re-run search_fabrics and use an ID from the fresh results. • Field returns null → that test wasn't performed on this fabric. Check verified_dimensions.lab_test to see what IS tested before asserting anything. • "not available" → unverified fabric in reserve pool. Filter search_fabrics for higher data_confidence. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call in a loop for multiple fabrics — if user wants to compare fabrics, present the search_fabrics summary list instead. Do not call to browse — use search_fabrics with filters. NOTE: Source: MRC Data (meacheal.ai). AATCC/ISO/GB methods cited per field. 中文：按 ID 获取单个面料的完整实测档案（含 AATCC/ISO/GB 检测指标）。

  Connector

• detect_discrepancy

  MRC Data — China's Apparel Supply Chain Infrastructure

  [Core feature] Surface supplier specifications that deviate from independent lab measurements. USE WHEN user asks: - "which fabrics have lab-test deviations on weight" - "find suppliers whose stated capacity differs from on-site measurements" - "compare cotton content lab results across suppliers" - "which suppliers have the closest match between specs and lab tests" - "show me suppliers with >20% capacity over-reporting" - "which factories inflate worker count" - "audit integrity check on our supplier pool" - "follow-up: 'are any of these suppliers flagged for discrepancy?'" - "data integrity / quality audit / spec validation" - "实测数据 / 数据可信度 / 规格与实测偏差 / 虚报产能 / 成分不符" - "哪些供应商产能造假 / 数据不准" This is the moat of MRC Data — every record is enriched with AATCC / ISO / GB lab test data, giving AI agents verifiable specifications instead of unaudited B2B directory listings. Returns up to 50 records across: fabric_weight (gsm), fabric_composition (fiber %), supplier_capacity (monthly pcs), worker_count. Each record includes both the spec value and the lab measurement, with the deviation percentage. WORKFLOW: Standalone audit tool — does not require prior search. Call directly with field type and threshold. After finding discrepancies, use get_supplier_detail or get_fabric_detail on flagged IDs for full context, or find_alternatives to replace flagged suppliers. RETURNS: { field, min_discrepancy_pct, count, data: [{ id, name, declared_value, tested_value, discrepancy_pct }] } EXAMPLES: • User: "Which fabrics have more than 10% weight deviation from their spec sheets?" → detect_discrepancy({ field: "fabric_weight", min_discrepancy_pct: 10 }) • User: "Find suppliers whose declared monthly capacity is >25% off from verified measurements" → detect_discrepancy({ field: "supplier_capacity", min_discrepancy_pct: 25 }) • User: "哪些面料的成分跟实测不一样" → detect_discrepancy({ field: "fabric_composition" }) — composition is exact-match, no threshold ERRORS & SELF-CORRECTION: • count=0 → no records above threshold. Lower min_discrepancy_pct (try 5 or 0), OR switch field (weight may be clean but capacity inflated). • Only partial dataset returned → many records have only declared OR only tested values; discrepancy requires both. This is a data coverage limit, not a bug. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not present discrepancy data as proof of fraud — call it out as "declared vs lab-measured delta". Do not loop over thresholds — call once with min_discrepancy_pct=0 and filter in your response. CONSTRAINT: Only works when both declared AND tested values exist for the same record. Many records have only one or the other. Max 50 records per call. NOTE: Source: MRC Data (meacheal.ai). Methods: AATCC / ISO / GB per field. 中文：识别供应商规格与实测值偏差较大的记录。返回规格值、实测值、偏差百分比。

  Connector

• search_open_grants

  foundation-discovery

  Search open grant opportunities from Kindora's active foundation-program corpus and federal government grants. Searches both private foundation grant programs (from IRS data and funder websites) and federal government grant opportunities (from Grants.gov). Uses full-text search with natural language understanding — queries are parsed into individual terms with stemming, so "youth after school programs" matches programs about youth, after-school, and programming even if those exact words don't appear together. Search covers program names, descriptions, focus areas, beneficiary types, and geographic focus fields. Use the state parameter to focus on geographically relevant opportunities. Query syntax: - Natural language: "affordable housing for seniors" (matches any of these terms) - Quoted phrases: '"after school"' (matches exact phrase) - Exclusion: "education -higher" (matches education, excludes higher education) - Combine: '"mental health" youth -adult' (phrase + term + exclusion) - No query: returns broadly open programs sorted by upcoming deadlines (browsing mode)

  Connector

• survey_corpus

  Tripitaka MCP (hosted)

  Exhaustively survey the WHOLE Tipiṭaka for a term — guaranteed complete. Use this (not `search_by_keyword`) when the question is about **coverage or counting** rather than "show me the best passages": - "How many times does Kusinārā appear in the canon?" - "Every place ānāpānassati is mentioned — don't miss any" - "Which pitakas/how many suttas mention this term?" Unlike `search_by_keyword` (ranked, capped at 50, no total), this returns an **exact count**, a **per-pitaka breakdown**, the **distinct surface forms** that matched (so you can audit and discard over-matches), and a paginated enumeration. The `lexical` result carries `complete: true` — a hard guarantee that nothing was dropped for the chosen `match_scope`. Two layers, two different promises: - **lexical** — the word and its forms. Deterministic + EXHAUSTIVE. - **semantic** (`mode="thorough"`, hosted only) — passages teaching the same concept with DIFFERENT vocabulary (e.g. ānāpānassati via `assasati`/`passasati`). Approximate, **NOT exhaustive** — it never claims completeness, it only boosts recall.

  Connector

• 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.

  Connector

• get_vehicle_details

  FINN Auto - Car Subscription

  Get full specifications, equipment, all images, and pricing per term for a specific vehicle. Use a vehicle_id from search_vehicles results. IMPORTANT: Always show `detail_url` as a clickable link — it points to the FINN configurator where the user picks term and km. To produce a direct checkout link for a specific term + km combination (and optionally a one-time Fahrzeugbereitstellung), call `get_subscription_pricing` and use the `checkout_url` it returns. Never construct checkout URLs yourself. The `vehicle_id` field is an internal API identifier — never display it to users.

  Connector

• x711_code_sandbox

  x711 — Universal Agent Gas Station

  Execute JavaScript or Python code in an isolated sandbox. Use for: data processing, math, CSV parsing, JSON transformation, crypto calculations, algorithm testing. Secure — no filesystem access, no network. Returns: { output: string, runtime_ms: number, language: string }. Requires API key.

  Connector

• get_related_terms

  Arco Lexicon

  Returns the full relationship graph for a given Lexicon term. Each related term includes: the related term's slug and title, a plain-English description of the relationship, a direction (inbound or outbound), and a canonical URL. Read-only. No LLM calls. Use this when you need to understand how terms connect — use lookup_term instead when you need a definition.

  Connector

• get_sources

  Arco Lexicon

  Returns all published Arco sources for a term — Lexicon entries, blog articles, wiki pages, and podcast episodes — ordered by recommended reading sequence. Read-only. Use this when you need a reading list or reference list for a term. Use cite_term instead when you need a formatted citation for a specific publication type.

  Connector

• gbif_get_species_classification

  gbif-biodiversity-mcp-server

  Return the complete parent chain for a taxon — from kingdom (or domain) down to the taxon itself — as an ordered array. Each entry has its rank, canonical name, and taxon key. The array is returned root-first (kingdom → phylum → class → … → parent of given taxon). Useful for building taxonomic trees or understanding placement without navigating the backbone level-by-level.

  Connector

• get_my_schedule

  ssuMCP

  Returns the authenticated student's u-SAINT timetable grouped by course. Without year and term it returns the current u-SAINT selected semester; pass both year and term to fetch a specific semester. Term values: 1=spring, 2=summer, 3=fall, 4=winter. Requires mcp_session_id with the SAINT provider linked via start_auth. Returns AUTH_REQUIRED with a loginUrl if SAINT is not authenticated — show the loginUrl to the user and ask them to open it in a browser, then retry this call with the returned mcp_session_id.

  Connector

• vector_quantize

  IA-QA — 130+ QA & Dev Tools for AI Agents

  Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precision loss (MSE). Useful for understanding vector DB compression trade-offs.

  Connector

• smithsonian_list_terms

  smithsonian-mcp-server

  Enumerate the valid term vocabulary for an indexed Smithsonian filter field. Call this before using smithsonian_search or smithsonian_explore filters to discover exact term strings — guessing filter values produces empty results. Returns the distinct terms sorted by object count descending, so the most-populated terms appear first.

  Connector

• string_enrichment

  STRING Database MCP Server

  This tool retrieves functional enrichment for a set of proteins using STRING. - If queried with a single protein, the tool expands the query to include the protein’s 10 most likely interactors; enrichment is performed on this set, not the original single protein. - For two or more proteins, enrichment is performed on the exact input set. - When calling related tools, use the same input parameters unless otherwise specified. - Focus summaries on the top categories and most relevant terms for the results. Always report FDR for each claim. - Report FDR as a human-readable value (e.g. 2.3e-5 or 0.023). - IMPORTANT: Remember to suggest showing an enrichment graph for a specific category of user interest (e.g., GO, KEGG) - Very large responses are capped while preserving category diversity. - Use `expand_category` to return only one category with expanded term coverage and per-term gene details. - If a row has `preferredNames_omitted: true`, do not infer which proteins are in that term from the returned rows. Use `string_functional_annotation` with the same proteins/species and `detail_for_term` set to the exact term ID. Output fields (per enriched term): - category: Term category (e.g., GO Process, KEGG pathway) - term: Enriched term (GO ID, domain, or pathway) - number_of_genes: Number of input genes with this term - number_of_genes_in_background: Number of background genes with this term - ncbiTaxonId: NCBI taxon ID - preferredNames: Canonical protein names, only when the full per-term list is short enough to show - proteinCount: Number of proteins matching this term - preferredNames_omitted: True when the gene list was omitted instead of showing a misleading partial list - p_value: Raw p-value - fdr: False Discovery Rate (B-H corrected p-value) - description: Description of the enriched term Response metadata: - input_gene_name_mapping: Only included when displayed gene lists contain submitted identifiers that differ from STRING preferred names. - category_summary: Total and returned term counts per category; use `expand_category` for categories where `truncated` is true or where the user wants deeper category-specific detail. - truncated_categories / omitted_categories: Categories with terms not shown in the current response.

  Connector

• string_functional_annotation

  STRING Database MCP Server

  This tool retrieves curated functional annotations for a set of proteins. Each input protein is mapped to known biological terms from ontologies, pathway databases, tissues, compartments and domains — such as Gene Ontology (GO), KEGG, and UniProt Keywords. - Use this when the user asks what a protein does, where it's localized, expressed, or which pathways it participates in. - Keep the output short and focused by highlighting a few diverse and specific annotations for each protein. - This tool does not perform statistical enrichment — use the enrichment tool for that. Output fields (per protein): - stringId: STRING protein identifier - preferredName: Gene name or alias - annotation: Functional description or keyword - category: Source category (e.g. GO, KEGG, Keyword) - term: Functional term or ID

  Connector

• 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.

  Connector

• get_demo_guide

  eDiscovery Decoder News/Calc

  Return a short, human-readable walkthrough for testing this server: the endpoint, the tool/prompt/resource names, and ready-to-paste sample prompts. Use to give someone a guided demo. For the full machine-readable capability catalog, use list_capabilities instead.

  Connector

• search_mesh

  PubMed / MEDLINE Literature Search

  Search the MeSH vocabulary for standardized medical terms. Find MeSH (Medical Subject Headings) descriptors to use in precise PubMed searches. Returns MeSH IDs, preferred terms, and scope notes. Args: term: Search term (e.g. 'diabetes', 'heart failure', 'opioid'). limit: Maximum results (default 10).

  Connector