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395,274 tools. Last updated 2026-08-05 08:00

"Understanding the term 'flux' or its various applications" matching MCP tools:

  • Browse Smithsonian objects within one exact category — a single museum (mode "museum"), culture, indexed date term (mode "period"), object type (mode "medium"), or subject term (mode "topic"). The value must be an exact indexed category term, not free text: resolve museum, culture, period, and topic vocabulary with smithsonian_list_terms first (object_type is not enumerable there — harvest it from smithsonian_search_objects results, and treat each casing as its own category, since a harvested object_type covers only the casing it was written in). Returns the category total count, a page of matching objects, and a museum breakdown of that page; page the full category with start and rows. For open-ended or topic discovery, start with smithsonian_search_objects instead.
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  • Look up WEO Contribution Architecture vocabulary — the contributor programme's terms, credit classes, and governance provisions (e.g. "Delta Credit", "Founding Observer", "Observer Network", "rate card", "malinformation"). Returns the term's context, its section anchor, and a deep link into the self-hosted CA edition. Omit `term` for programme status: phase, activation criterion, current corpus size, and enquiry address. Use to resolve participation vocabulary — the Contribution Architecture governs participation, whilst the Methodology Manual (`get_methodology`) governs what qualifies. Matching is exact-first, then substring; an unknown term returns a sample of available terms. Served in full on both tiers.
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  • Track every document added or changed in one RIS application within an exact date window (changed_from/changed_to), optionally including deletions (include_deleted) — the delta-sync and monitoring primitive for mirrors and watchers, and the only surface that reports removals. Unlike the search tools’ coarse, additive-only changed_since intervals, this is exact-dated and deletion-aware. application takes any RIS application code (e.g. BrKons, Dsk, BgblAuth); the four applications with a different History-feed name are mapped automatically. Each changed document comes back in a compact cross-class record — document_number (for ris_get_document), title, dates, binding_status, and rendition URLs — plus its last-changed date; removed documents come back as deleted records with a deletion timestamp. One application per call; page explicitly for large windows. Application codes and coverage: ris_list_reference topic applications.
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  • Create a new application (workspace) owned by the caller. Requires a personal API key (usr_...) — application-scoped keys cannot create applications. Seeds default flows unless skipDefaultFlows is true. Creates persistent state and is NOT idempotent: calling it twice creates two applications. Returns the new application id, which you then pass as applicationId to the other tools.
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  • 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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  • Enumerate the valid term vocabulary for an indexed Smithsonian filter field (unit_code, culture, place, date, online_media_type, topic). Terms are a controlled vocabulary — often plural or qualified (e.g. "Paintings", not "Painting") — so guessed filter values tend to return nothing. Returns a page of the field's distinct term values; large vocabularies (topic has 133k terms, place 114k) page via start and rows. For unit_code, each code is returned with its museum name and contains matches the name as well as the code, so a museum name resolves to its code in one call.
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    An AI-powered MCP server that enables natural language interaction with AO (Arweave Operating system) for creating, running, and testing code and handlers without manual coding.
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  • Official FLUX MCP server from Black Forest Labs. Generate, edit, vary, and browse FLUX.2 images directly in any MCP-compatible client.

  • MCP server for Flux AI image generation

  • Edits an existing image guided by a text prompt. Pass a public `imageUrl` plus a `prompt` describing the change ("add a moon to the sky", "swap the background for a neon city", "make it look like a comic panel"). Submits, polls, and returns the edited image URL(s). Default model is 'grok-imagine-i2i' (6 cr per call, returns 2 variations, ~30s, best cost-to-quality on standard edits). Other I2I-capable models: 'seedream-v4-edit', 'wan-2.5-spicy-i2i', 'flux-kontext-pro', 'qwen-image-edit', 'gpt-image-1.5-i2i' (slow, ~5min). Use list_image_models for full lineup. Note: source URLs with spaces or parentheses may fail upstream; prefer clean URLs. ## Model selection guide for edits Default: `grok-imagine-i2i` (6 cr per call, returns 2 variations = 3 cr/image effective, fast ~30s, strong general-purpose edit quality). Pick a different model when: - Need a single deterministic output, or 4K resolution -> `seedream-v4-edit` (7 cr per image, supports 1K/2K/4K, multi-image up to 6) - Subtle edits / preserve composition / character consistency -> `flux-kontext-pro` or `flux-kontext-max` - NSFW edits -> `wan-2.5-spicy-i2i` - Highest quality, time is not a concern (~5 min OK) -> `gpt-image-1.5-i2i` or `grok-imagine-quality-i2i` (16 cr @ 1K, 22 cr @ 2K) - Stylized / artistic transformation -> `midjourney-i2i` If the user simply says "edit this image" with no other signal, default to `grok-imagine-i2i`.
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  • Quick company lookup: facilities (with addresses and operations) and enforcement actions (recalls) for a single company and its known aliases. Costs 1 credit. Excludes: 510(k) clearances, PMA approvals, drug applications, inspection history, and subsidiary data. Related: fda_company_full (adds clearances/approvals/drugs for 5 credits), fda_suggest_subsidiaries (discover related entities), fda_get_facility (per-facility products and operations by FEI).
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  • Get the international / foreign footprint of a U.S. trademark by serial number: its Madrid Protocol international registration (IR number, date, status, renewal), whether it was filed as a 66(a) Madrid extension, Madrid maintenance (§8/§15, renewal), and the foreign applications/registrations it claims as priority or basis (country, registration number, dates). Use this for ANY question about a mark's foreign, international, Madrid Protocol, WIPO, or EUIPO registrations. Data comes from USPTO records, so it is only available for marks with a USPTO record — it does not query WIPO/EUIPO live.
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  • Plain-language definitions of industry terms in a service category (e.g. SEER2, AFUE, AHRI match). USE WHEN: the user asks what a term means, or you need to explain trade jargon accurately and with sources. ARGS: `category`; optionally `term` (a slug) for one definition — omit to list. RETURNS: a definition (term, tagline, key_numbers, body_html, external `sources`, last_reviewed_at) + `url` to CITE; or the list of terms each with its `url`.
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  • What VenuMark is (the food vendor application and compliance platform for Florida events), how the workflow runs, current pricing tiers, and which tier fits an organizer. Use when someone asks about running vendor applications, pricing, or whether VenuMark fits their event.
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  • Keyword/full-text search over the Canton Network knowledge base (CIPs, Canton/Daml/Splice docs, forum, mailing lists, whitepapers, grant proposals, blog, YouTube, GitHub). Canton-specific. Do NOT use for other blockchains, the web, or local files. Use this for exact-term/name lookups; use semantic_search instead for conceptual or 'how does X work' questions, and get_doc to read a full page once you have its id.
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  • 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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  • 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 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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  • 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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  • Fetch records from any India Open Government Data (data.gov.in) resource by its resourceId. Supports pagination, per-field filtering, field projection, and sorting. The resourceId is the UUID shown on a dataset's page on data.gov.in (and in its API URL, e.g. api.data.gov.in/resource/<resourceId>). Example resourceId 9ef84268-d588-465a-a308-a864a43d0070 is "Current Daily Price of Various Commodities from Various Markets (Mandi)" with fields like state, district, market, commodity, variety, grade, arrival_date, min_price, max_price, modal_price. Use resource_meta first if you do not know a resource's field ids.
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  • List applications across all accessible jobs. Supports filtering by candidate, job, stage, status, AI score range, and date ranges. Use for pipeline analytics, sync jobs, and ATS dashboards. Avoid include=candidate or include=cv.text on large pages (each embeds heavy nested data); if the response exceeds the budget the tool returns isError:true with error_code=response_too_large and retry hints. Each application embeds its current `stage` (IdName) directly in the response — this is sufficient for rendering kanban/pipeline views; you DO NOT need to call hires_get_job to fetch workflow_stages separately when rendering a pipeline.
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  • Look up the WEO methodology definition for any platform-specific term, field, or concept (e.g. "CC-V", "T2", "PAA", "ENT-1", "PIET"). Returns the term's definition, its section anchor, a deep link to that section of the published methodology, and the methodology version. Use to resolve any vocabulary the other tools return. Pass `term`; matching is exact-first, then substring, and an unknown term returns a sample of available terms.
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  • 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.
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