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198,348 tools. Last updated 2026-06-13 07:03

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

  • 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`.
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  • 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.
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  • Generate a complete colour direction package for another AI agent or image generation model. Fetches a historically grounded archive palette from the concept, then produces: an agent brief (colour direction in prose), colour tokens with hex values and roles, a model-specific image generation prompt, a negative prompt, and lighting notes. Supports midjourney, flux, dalle, stable_diffusion. Example: task='luxury hotel bedroom', concept='Ottoman winter luxury', model='midjourney'. Use this to make Colour Memory the colour layer for other AI systems.
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  • 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.
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  • 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.
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  • 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).
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Matching MCP Servers

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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

  • Returns GhostRoute's ownership-graph record for an autonomous system: the registrant/parent organisation, its HQ country and sovereign zone, RIR, and cloud/AI-infrastructure flags. The long-term moat — who actually owns the network a route originates from. Use this tool when: - You have an origin ASN and need its corporate owner + jurisdiction. - You are assessing whether an ASN belongs to a cloud front or the real operator. Inputs: - `asn` (path, required): AS#### or a bare AS number. Returns: - `registrant_org`, `parent_org`, `parent_org_country`, `sovereign_zone`, `rir`, `is_cloud_provider`, `is_ai_infrastructure`, or `{matched:false}`. Latency: - Typical <300ms (cached corpus read, RDAP fallback on a miss).
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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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  • Comprehensive air quality assessment for a location in one call. Combines nearby monitor discovery and current readings with DAQI into a single response. Use this as the first tool call for any air quality question about a location. For long-term trend analysis, use the dedicated `trend_analysis` tool. Returns a structured 'summary' dict with purpose-appropriate sections. Present the summary description to users first. Args: location: Postcode, place name, or "lat,lon". purpose: What the user needs — "general" (default), "health" (safety/worry), "exercise" (outdoor activity), or "planning" (homebuying/school assessment/long-term).
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  • Capture a PNG screenshot of the page or a specific element. Returns base64-encoded image bytes AND a file_id (persisted in DialogBrain files storage). Pass file_id straight to messages.send(attachment_file_ids=[file_id]) — do NOT call files.upload again. Use sparingly — favor browser.snapshot for structured DOM understanding.
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  • Detects potential LLM jailbreak attempts by analyzing user input against NIST AI Risk Management Framework adversarial patterns. Designed for persona risk assessment, this tool evaluates text for common jailbreak techniques such as prompt injection, role-playing, or obfuscation. Inputs include the user message and optional context, returning a risk assessment with confidence scores and pattern matches. Ideal for real-time moderation in chat applications or API gateways.
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  • 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.
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  • Preferred user-facing Google Ads search-terms analysis tool. Renders the search-terms analysis dashboard and can either take analysisPayload from google_ads_analyze_search_terms or fetch the analysis directly when called with search-term-analysis arguments.
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  • "All phenotypes under [HP:N]" / "full subtree of [HPO term]" — transitive descendants (children, grandchildren, …) of an HPO term. Use for exhaustive coverage (e.g. "every cardiac phenotype" via descendants of HP:0001626).
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  • Show which quality dimensions matter for a stated purpose, WITHOUT ranking any models. Returns the inferred weights and the discovery-walk trace. Useful for understanding how XFMS interprets the purpose before committing to a pick.
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  • Podcast search by term. Find podcasts in the open podcast database by name or keyword. Returns matching podcasts with title, author, description, categories, episode count, and artwork. Example: search_podcasts({ query: "true crime", max: 10 })
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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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  • View applications for your listing. Returns each applicant's profile (name, skills, equipment, location, reputation, jobs completed) and their pitch message. Use this to evaluate candidates, then hire with make_listing_offer. Only the listing creator can view applications.
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  • Returns the complete Trident 2D specification including grammar, syntax rules, coordinate system, containers, nodes, connections, shapes, and icon reference. Use this when you need deep understanding of the Trident DSL.
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