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668,961 tools. Updated 2026-10-17 08:42

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

  • Update or create a flow. Use `dryRun: true` to preview any change without writing. Omit flowName to create: definition and displayName are required. Give flowName to update: displayName and/or definition and/or connectionReferences. SURGICAL EDIT: pass `operations` — an ordered list of set/add/remove/merge ops on array-of-keys paths — to change one action or parameter without resending the whole definition. Provide EITHER operations OR definition; connectionReferences may accompany either and lands in the same save. WHOLE definition: call get_live_flow, mutate properties.definition, pass it here. RENAME: pass displayName alone. Power Automate rejects a PATCH carrying no flow content, so this re-saves the unchanged definition — the maker portal does the same. definition.description is required, and must be brief — we append " #flowstudio-mcp" to it.
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  • Get a single schedule by slug or id, including its cron status (last_published_version, next_run_at, last_run_id) and the definition body of its current version (published if any, else latest). Use get_schedule_version to read a specific older version.
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  • Look up one glossary term by record ID ('term:abc123'), permalink, or exact name and return its full definition and facets. Use it when the user asks 'what is <term>'; to find terms by topic, or across all content, use search_esg or search_content, and to survey the glossary use list_terms. Name matching is exact (case-insensitive) with no fuzzy or partial matching, and it returns a single term, never a list. The `definition` field is the authoritative text and `facets` carries the topic/content_type classification; an unknown identifier returns NOT_FOUND. Cached ~10 minutes; rate-limited per IP; retry on 5xx.
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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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  • Methodology + reference, versioned server-side (re-fetch rather than caching long-term). Arg-driven: * `field` given — the plain-English DEFINITION + role of a signal field (deterministic lookup, no LLM). e.g. field="mom_60". The response's `available_fields` lists every documented field. * `name` given — a methodology playbook (markdown) by name, OR two special reference pages: - name="schema" (or "data-contract") -> the machine-readable substrate DATA CONTRACT: every outcome/label column with its leakage classification (feature|label|opportunity| regime_telemetry|identity) and as-of boundary. Only `feature` columns are safe as selection inputs. - any other name -> the playbook markdown (start-here, daily-workflow, run-your-own-tournament, exit-lab, leakage-and-data-contract, changelog). * neither — the CATALOG of published playbooks (name/title/summary), plus a pointer to the field dict (`field=`) and schema page. Args: name: playbook name, or "schema"/"data-contract" for the data contract. field: a signal field name to explain (overrides `name`).
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  • Read back the full stored definition of an api template version by slug or id. Returns the exact definition body that was authored (as a JSON string, so you can parse it to diff or patch and resubmit as a new version) plus its checksum. Omit version to get the latest version.
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    A persistent, self-organizing memory MCP server for AI assistants, using semantic search, knowledge graphs, and reinforcement learning to automatically manage and retrieve memories.
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    MIT

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  • Read back the full stored definition of a dynamic endpoint version by slug or id. Returns the exact definition body that was authored (as a JSON string, so you can parse it to diff or patch and resubmit as a new version) plus its checksum. Omit version to get the latest version.
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  • Calculation, not advice. Verify with a professional before acting. Deterministic mortgage refinance break-even analysis. Given your current loan (balance, rate, remaining term) and a refinance offer (new rate, new term, closing costs, optional points), computes: - monthly P&I savings; - the cash-flow break-even month (Senaro's formula: total refinance cost divided by monthly savings, rounded up to a whole month, built on the payback test the CFPB toolkit describes); - the interest delta over your remaining-term horizon, and each loan's interest over its whole schedule (the current loan's remaining term, the new loan's full term); - a term-matched scenario that isolates the rate cut from a term reset; - a term-reset-trap flag (a longer term and a lower payment, but more interest over the new loan's full term than over the current loan's remaining term); and - the economic break-even (net-worth crossover) month under an equal-outflow model: both sides spend the same each month; the side that keeps the current loan starts with the upfront refinance cost invested at month 0 (nothing when costs are rolled in); money not spent on a payment is invested at investment_return_pct. The search runs to the end of the longer loan term, and stops earlier where a loan's term ends with money still owed. Rate-and-term refis only (cash-out and tax effects are out of scope). Pick this when refinancing your existing mortgage into a new rate and term is the question; pick `compare_mortgage_terms` when comparing two mortgage structures on a purchase you have not yet taken out. All defaults cite primary sources (LodeStar/ALTA closing-cost data, the CFPB toolkit's payback test). Scalar output, no chart series. payoff_months and payoff_month_shift use {status, value, explanation}; the two break-evens use {code, month, explanation}.
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  • Add terms to one storefront's corpus, or take terms out of it. Add for a term the corpus SHOULD hold and does not — a rival's brand, a phrase Apple has never measured the popularity of, the term a thin storefront is really about; a pinned term is kept through every rebuild and refreshed on the same 72-hour cadence as the rest. Remove for a term that does not belong: ANY term can go, and one the heuristic found is also kept out of future rebuilds rather than returning within 72 hours. This is NOT `save_market`: seeds are what the corpus is expanded FROM and changing one re-runs the whole heuristic over that storefront, where these two writes act on single terms in the result.
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  • Quote a specific proxy plan configuration. The actual price of a chosen `product_id`, country distribution and add-ons, for a customer comparing real prices or about to buy. What Webshare sells in general is in the catalog (list_proxy_catalog), and a configuration outside the bounds get_customize_options reports is refused with a 400. BEHAVIOR: `behavior="add"` prices a new plan alongside the customer's current ones; `behavior="upgrade"` prices changing the plan named in `plan_id`. The two return different numbers for the same configuration. Either one restarts the subscription's term today for every active plan, so the quote charges all of them for a fresh term and gives back the unused part of the current one as credit. TERMS: pass `term="monthly"` or `term="yearly"` for a single quote, or omit `term` to get both terms in the same response, side by side. FREE PRODUCT: rejected with a ValidationError — the free tier is auto-provisioned and not purchasable. AUTHENTICATED VS ANONYMOUS: with `user_id` the response carries `paid_today`, `credits_added` and `credits_used`, which account for the plans the customer already holds, and `tax_breakdown` (when the customer has a payment method on file). Without user_id the response reflects a first-time anonymous purchase quote.
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  • Apply an ImportCluster YAML declaratively (the twin of `ankra cluster apply`): the cluster named in metadata.name is created if it does not exist, and its definition is REPLACED by the manifest - every stack in the manifest is created or updated and stacks the manifest does not name are pruned. Read the current definition with export_cluster_iac first and validate with validate_cluster_manifest. Moving the cluster to a different GitOps repository or branch is refused until allow_repoint is true (and allow_repoint_destroying_data when PersistentVolumeClaims would be pruned). For one stack at a time prefer create_stack / update_stack.
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  • Submit a new glossary term for human review. Nothing is published immediately: the call creates a pending proposal and returns a `proposal_id`; a reviewer decides whether it goes live, and only an approved term later appears in get_term. Use it only when the user explicitly wants to contribute a term; to look one up use get_term. `name` is the display name and `definition` must be at least 10 characters; `facets` is optional and its values should come from get_esg_metadata / list_industries vocabularies. Requires a write token in ESG_HUB_WRITE_TOKEN — a missing or invalid token returns 401 — and calls are rate-limited.
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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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  • Read one existing Dutch vehicle definition by lowercase term slug or term.<slug>. Use search_vehicle_knowledge for free text or an unknown key, get_answer_context for a known answerId, or get_model_context for model-specific context. No fuzzy definition generation. Unknown valid key returns TERM_NOT_FOUND; uppercase/spaced inputs fail validation before normalization. Public read-only access; no knowledge writes. Calls consume rate limits.
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  • Read back the full stored definition of an app version by slug or id. Returns the exact definition body that was authored (as a JSON string, so you can parse it to diff or edit and resubmit as a new version) plus its checksum. Omit version to get the latest version. This is the EDITING view; get_app returns the typed published manifest for reading.
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  • Get the JSON Schema a workflow definition must satisfy. Fetch this before authoring the definition for create_workflow / create_workflow_version.
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  • Returns the full definition, communication style and behavioural guidelines for one named persona from the user's NC persona set. persona_name takes the value returned as persona_hint by recall_context. A name outside the user's persona set returns a PERSONA_NOT_FOUND error rather than a substitute or an invented definition.
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  • Add protected terms to the account, or update existing ones (matched by exact term text). Every later protected translation on the account (translate, translate_batch and the clinical tools) keeps these terms exactly as written, or uses `translations.he` / `.en` / `.ru` for that target language when set. Sending a term that already exists replaces its translations and note. Up to 2,000 terms per account, each up to 200 characters. Changes apply to new requests within 30 seconds. Free.
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  • WRITE: replace the content of one glossary term (the term list and any flags you pass overwrite the old values). Needs the Reviewer role. Read the term first with localazy_get_glossary_term. PUT /projects/{projectId}/glossary/{id}.
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  • Lists published glossary entries covering product management, agile delivery, user research, and product thinking terminology, including term names, comprehensive definitions, and metadata. Returns all glossary entries in one unpaginated response. Use it for term definition questions ("what is X?"). For articles explaining or applying a method, prefer search_articles.
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  • [tourradar] Search tour reviews using AI-powered semantic search. Requires tourIds to scope results to specific tours. Use this when the user asks about reviews, feedback, or experiences for specific tours. Combine with an optional text query to find reviews mentioning specific topics (e.g., 'food', 'guide', 'accommodation'). When you don't have tour IDs, use vertex-tour-search or vertex-tour-title-search first to find them.
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