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482,849 tools. Updated 2026-08-27 22:39

"Definition or Uses of the Term 'Vertex'" matching MCP tools:

  • Get one principle cluster by stable slug. Returns the cluster definition, shared rationale, and the full set of member principles (slug + title) so the caller can pivot into principles.get without a second list call. WHEN TO CALL: the user has already named a specific cluster (e.g. 'delegation', 'visibility', 'trust', 'orchestration') OR you have a slug from a prior clusters.list / principles.list response and need its full definition + member principles. The response embeds member principle slugs + titles already, so DO NOT loop principles.get over each member to get a cluster overview — read the response. WHEN NOT TO CALL: the user is describing a topic, failure mode, or keyword in natural language (call principles.search instead); the user wants to discover which clusters exist (call clusters.list); the user wants the definition of one specific principle (call principles.get directly). Idempotent + cacheable per slug. Returns 404-shaped error_payload on unknown slug — the slug must match exactly the value emitted by clusters.list, with no normalization.
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  • 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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  • Update or create a Power Automate flow via the live PA API. If flowName is omitted or blank, a new flow is created (PUT with a generated GUID) using an environment admin account — definition and displayName are required in that case. If flowName is provided, the existing flow is PATCHed: displayName and/or definition and/or connectionReferences are updated. RENAME: pass displayName alone, or alongside definition/operations. Power Automate rejects a PATCH that carries no flow content, so a displayName-only (or connectionReferences-only) save re-saves the flow's unchanged live definition and connectionReferences — the maker portal does the same full save on rename. The response `updated` list names only what you asked to change. SURGICAL EDIT: instead of resending the whole definition, pass `operations` — an ordered list of set/add/remove/merge ops on array-of-keys paths — to change one action/parameter on a large flow cheaply and safely (fetches the live definition, applies the ops, PATCHes the result). Provide EITHER operations OR definition. Use `dryRun: true` to preview the result without writing. Mirrors displayName changes into the Power Clarity cache (gFlows). To modify a WHOLE definition: call get_live_flow, mutate properties.definition (including its description), pass it here. The flow description lives at definition.description and is required; we append " #flowstudio-mcp" to it for usage tracking.
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  • Return a canonical definition for a primitive Eurorack / synthesis concept and its relations to other concepts in the corpus. Use this for VOCABULARY questions, not module questions — when the user is asking what a term means or how two terms relate, not which modules implement it. Typical shapes: - "Is four-quadrant mult the same as through-zero AM?" → lookup_concept("four-quadrant mult") - "What's the difference between a gate and a trigger?" → lookup_concept("gate") - "Modular signal level vs line level — when does it matter?" → lookup_concept("modular signal level") - "Are clock dividers just pulse counters?" → lookup_concept("clock divider") - "Are polyphonic patch cables TRRRRRS?" → lookup_concept("polyphonic cable") Lookup is case-insensitive across three axes, tried in order: the canonical id ("through-zero-fm"), the canonical label ("Through-Zero FM (TZFM)"), and any registered alias ("tzfm", "through zero fm"). Spaces and hyphens are matched literally; the lookup does NOT normalize whitespace beyond lowercasing. If the term doesn't match anything, the response includes up to 5 substring-matched suggestions. Args: - name (string, required, min length 2): the term to look up. Examples: "AM", "ring mod", "four-quadrant mult", "TZFM", "clock divider", "gate", "trigger". Returns: { "concept": { "id": "amplitude-modulation", "label": "Amplitude Modulation (AM)", "description": "A multiplication of two signals: the carrier...", "aliases": ["am", "amplitude modulation", "amplitude mod"], "related_concepts": [ { "related_concept_id": "ring-modulation", "related_concept_label": "Ring Modulation (RM)", "relation_kind": "commonly_confused_with", "note": "AM with a unipolar modulator preserves the carrier..." }, ... ], "source_id": null, "citation_url": "https://learningmodular.com/glossary/...", "citation_quote": "Amplitude modulation is when..." } | null, "_meta": { "query": "<the name argument verbatim>", "matched_via": "id" | "label" | "alias" | "none", "concept_suggestions": [ { "id": "...", "label": "...", "matched_via": "alias", "matched_text": "..." } ], "feedback_hint": "...?" } } Relation kinds: - "related_to" — see-also link (default; symmetric in spirit). - "subtype_of" — X is a specific case of Y (RM ⊂ AM, TZFM ⊂ linear FM). - "inverse_of" — X is the opposite of Y (clock-divider ↔ clock-multiplier). - "commonly_confused_with" — they're distinct, but people conflate them (gate vs trigger, AM vs RM, modular level vs line level). When to cite: every concept carries either source_id or citation_url + citation_quote. Surface the citation when the answer affects a decision (e.g. "the corpus cites learningmodular.com — TRS cables are physically the same connector whether carrying balanced mono or unbalanced stereo; only the destination determines the role"). When the result is null and concept_suggestions are provided, present 2–3 closest matches to the user. If none look right, the corpus genuinely doesn't carry that concept — call report_gap with kind="missing_field" and tool_name="lookup_concept" naming the term and its expected definition.
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  • Look up the 99 Names of Allah (Asma ul Husna). Returns Arabic, transliteration, English and Bengali. Give a number for one name, a search term to match by meaning or transliteration, or neither to get all 99.
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  • Rollback a skill to a previous version recorded by list_skill_versions. The current definition is replaced and cannot be recovered except by rolling forward to another stored version. Requires full or skills:write permission. Do not use delete_skill when you only need to revert. Pass playbook_id as the UUID or GUID of the playbook this call should target.
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  • Returns the JSON Schema for an MP Scene document. Use this to construct valid scenes from scratch or to remind yourself of the exact shape of layers, animations, effects, and transitions before calling picsart_media_validate_scene or checking a frame with picsart_media_contact_sheet. The returned schema is authoritative; any document that validates against it is accepted by the renderer. Pass `name` (e.g. "MpMediaContent") to get back just that one definition instead of the full ~66 KB schema — its internal $refs point into the full schema's #/definitions.
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  • List your org's TAGGING RULES (the dashboard's 'Tagging rules') — labels applied to posts your Watchers already ingest. NOT the dashboard's Keyword Monitor: for the keywords that search all of Reddit daily, use keyword_monitor_list. Each rule tags matching Dataset records whose title or body mentions its term as a whole word. Returns the term, active status, and match statistics. Changes take effect on the next scheduled processing cycle. Existing opportunity scores and matches are not retroactively updated. (requires a free Prowlo account — call it to get a signup link)
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  • Submit a multi-step workflow to the Botverse workflow engine. Steps execute in dependency order; parallel branches (multiple steps with the same depends_on) run simultaneously. Returns a workflow_id immediately — poll get_workflow_status every 5–10 seconds until terminal. INTER-STEP REFERENCES: pass a prior step's output into a later step with the string "$.steps.<step_id>.output_key" (e.g. a docx→pdf chain: step to_pdf has depends_on: ["to_docx"] and inputs {"source_url": "$.steps.to_docx.output_key", "input_format": "docx", "output_format": "pdf"} using tool convert_from_url). Workflow params are referenced as "$.params.<name>". No other template syntax (${...} etc.) is supported. BILLING: convert-only workflows run on wallet balance ($0.05/step). Workflows containing transcode or transcribe steps require auto-refill to be enabled at botverse.cloud/dashboard/billing (their cost scales with source duration). Workflow definition uses BWDL (Botverse Workflow Definition Language) — schema at botverse.cloud/schemas/workflow/v1.json.
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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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  • 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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  • Return the official legislative reasoning (Gesetzesbegründung / explanatory memorandum from a Bundestags-Drucksache) for a specific German federal provision — the source for genetic/historical interpretation: *what the legislator intended* with this norm. Reach for it when the question turns on a norm's purpose (ratio legis / 'why does this rule exist'), the meaning of an unclear or undefined term, whether a regulatory gap is planwidrig (grounds an analogy) or a deliberate silence (grounds an Umkehrschluss), or what a specific amendment was meant to achieve — the everyday uses of materials in an Auslegung or a Schriftsatz. Weight: the Begründung is strong evidence of legislative intent but not binding, and the statutory wording stays the outer limit of any reading. Pass a provision like '§ 823 BGB' or 'Art. 87a GG'. Returns the linked Begründungs-Abschnitte newest first, each with its 'BT-Drs.' Fundstelle, the reasoning text, and the other norms the same change amended ('target_norm_keys'). Complements ``legal_find_citing_decisions`` (how courts apply a norm) with the drafting intent behind it. Covers amendments since 1949 to German federal law (``gesetze-im-internet`` corpus) only; an unknown or non-German provision returns no materials. Excerpts are prepared from the Bundestag-extracted text, not the original PDF.
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  • Search products by a term Arguments: term - the search term to look for products. It should be at least 3 characters long. cursor - optional, used for pagination. If provided, it will return the next page of results after. Pagination: Supports pagination with 'cursor' arguments. If 'cursor' is not provided, it will return the first page of results. Value for 'cursor' can be obtained from the 'nextCursor' field in the response. If 'nextCursor' is null, it means there are no more results to fetch. If value of cursor is null (or a string representation of 'null') dont send it in the payload. Results: Each product includes 'requiresFileUpload'. When true, the product has a required file-upload option (e.g. "upload your design") and shouldn't be added to cart through this assistant. Do not attempt to purchase it — tell the user it must be ordered on the website. Flow: - Call this tool with a 'term' argument and optionally with 'cursor' to search for products. - if you find a matching product, call 'get_product_details' with the product ID to get its variants and options (if any). - if 'requiresFileUpload' is true, inform the user the product needs a file upload and cannot be purchased here. - Call 'add_item_to_cart' with results of 'search_products' and 'get_product_details' (variant) tools to add the product to the cart. - [IMPORTANT] If product has variants ask user to pick
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  • Preview what a workflow WOULD do, without running it. Costs nothing and runs no models: returns the step execution order, each step's role/model/max_tokens and whether it pauses for a human checkpoint, the workflow's declared parameters (validated if you supply values), which required integrations your account already has credentials on file for, an upper-bound cost estimate, and definition_sha — a fingerprint of the definition this plan was built from, which tells you whether the definition changed between planning and running but never hands back the definition itself. Starts no session and records no usage. Use before run_workflow to check a pipeline fits before spending on it. Requires authentication.
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  • Validate a TypeScript intent definition without generating Swift. Runs the full Axint validation pipeline (134 diagnostic rules) and returns a JSON array of diagnostics: { severity: 'error'|'warning', code: 'AXnnn', line: number, column: number, message: string, suggestion?: string }. Returns an empty array [] when validation passes. Use: use for TypeScript DSL diagnostics before Swift output; use swift.validate for existing Swift. Inputs: source is TypeScript DSL text; strictness options affect diagnostics only and never emit Swift. Effects: read-only diagnostics; writes no files and uses no network.
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  • Get the data structure definition (DSD) for a dataflow: its dimensions and the valid codes for each, which you need to build a series key for get_series. Returns SDMX 2.1 structure XML. The DSD id differs from the dataflow id (e.g. dataflow BBEX3 uses DSD "BBK_ERX"). Pass the dataflow id (flowRef) and this tool resolves the DSD for you; the dimensions appear in <DimensionList> in key order. Set withCodes=true (default) to inline the codelists (references=children).
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  • Render a Mermaid diagram definition and return the image with metadata. The definition should be valid Mermaid syntax (e.g. flowchart, sequence, class, ER, state, or Gantt diagram). Returns a list of content blocks: the rendered image plus a JSON text block with metadata including a mermaid.live edit link for opening the diagram in a browser editor. Args: definition: Mermaid diagram definition text. filename: Output filename without extension. format: Output format — ``"png"`` (default), ``"svg"``, or ``"pdf"``. download_link: If True, return a temporary download URL path (/images/{token}) that expires after 15 minutes; if False, return inline image bytes. Defaults to True (URL) — set ``DIAGRAMS_INLINE_DEFAULT=true`` on the server to flip the default. SVG/PDF and PNGs larger than the inline limit always use a download link.
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  • Use this when the user asks for today's word, a daily vocabulary nudge, or a single-word warmup. Returns today's deterministic Word of the Day (definition, part of speech, example, synonyms/antonyms), optionally scoped to a test family (isee, ssat, sat, psat, gre, gmat, lsat, general). Do not use for arbitrary lookups — call get_definition instead.
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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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  • Find how the **suttas and Vinaya define a Pāli term in their own words**. The canon defines its own terms with fixed formulas — "Katamañca … dukkhaṁ?" (what is X?) … "ayaṁ vuccati … dukkhaṁ" (this is called X), "X adhivacana" (X is a designation for …), or the Vinaya "X nāma". This tool locates those definitional passages and returns them **cited**, so the assistant can present the doctrinal essence straight from the source. 🧭 **This tool vs `get_word_definition`:** - **`define_from_suttas`** → the *doctrinal* definition, how the term is defined **inside the canon**. Use for "how do the suttas define X", "what is the canonical definition of X", "define X from the suttas". Returns a few precise segments, not a lexicon essay. - **`get_word_definition`** → the *lexical* definition from dictionaries (Payutto / PTS / DPPN). Use for etymology and word meaning. They complement each other — offer both when the user wants the full picture (dictionary sense + how the Buddha defined it). 📖 **How to present the result:** Results are ranked; the top one is usually the canonical definition. **Quote the Pāli (and English where present) verbatim** and render each `cross_reference.tripitaka_mcp_reader.segment_url` as clickable markdown so the user can verify. Do NOT paraphrase into your own definition — the point is the canon's own words. Each result is tagged `kind` (direct / simile) and `detail` (descriptive / enumerative); a *descriptive* definition characterises the term, an *enumerative* one lists its types — prefer the descriptive when explaining the essence. ⚠️ A result tagged `context: true` **does not contain the term in its own line**. The canon's stock similes attach to a formula rather than to a word: the four jhāna similes (bath powder, deep lake, lotus pond, white cloth) never say *jhāna*, they illustrate the `vivicceva kāmehi …` formula that opens the paragraph. Such rows are found through that paragraph, so **say so when quoting one** — present it as the simile the passage uses, not as a line that defines the term.
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