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"Design Architecture of Ontology-based AI-PHM Using Life Acceleration Algorithms" matching MCP tools:

  • Computes a personal angel number from a birth date using the Pythagorean Life Path as the base. Life Path 1-9 maps to the triple sequence (LP 4 → 444). Master numbers 11, 22, 33 map to 1111, 2222, 3333 respectively. SECTION: WHAT THIS TOOL COVERS The personal angel number is the individual's primary energetic signature in angel number tradition. Derived using the digit-fusing Life Path method (same as asterwise_get_numerology_profile): all digits of the birth date are summed and reduced to a single digit or master number, then mapped to the corresponding triple or quadruple sequence. Returns the Life Path number, the angel sequence, and the full angel number interpretation. SECTION: WORKFLOW BEFORE: RECOMMENDED — asterwise_get_numerology_profile — confirm Life Path before calling. AFTER: None. SECTION: INPUT CONTRACT date: Birth date in YYYY-MM-DD format. Example: '1994-03-31' name (optional): Person's name for personalisation. SECTION: OUTPUT CONTRACT data.birth_date (string) data.life_path (int — 1-9 or master 11/22/33) data.angel_number (string — e.g. '333' for LP 3) data.number (string) data.theme (string) data.message (string) data.guidance (string) data.areas[] (string array) data.name (string or null — if provided) SECTION: RESPONSE FORMAT response_format=json — structured JSON. response_format=markdown — human-readable. Both return identical data. SECTION: COMPUTE CLASS FAST_LOOKUP — pure digit math, no ephemeris. SECTION: ERROR CONTRACT INVALID_PARAMS (upstream): Invalid date format → 422. INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_angel_number_today — collective daily number from today's date, not birth date. asterwise_get_numerology_profile — full Pythagorean profile; this tool extracts only the Life Path → angel sequence mapping.
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  • Execute a raw SPARQL SELECT query against the CELLAR Virtuoso endpoint. Use only when the curated tools (eurlex_search_documents, eurlex_get_relations, etc.) do not cover the needed traversal. The server caps all queries at 100 results — include an explicit LIMIT in your query to control the count; if omitted or above 100 it will be injected or capped automatically. The CDM ontology prefix is prepended automatically: cdm: = http://publications.europa.eu/ontology/cdm#. Also auto-includes skos: and xsd: prefixes. Requires familiarity with the CELLAR CDM ontology. Key predicates: cdm:resource_legal_id_celex (CELEX number), cdm:work_date_document (date), cdm:work_has_resource-type (document type), cdm:work_is_about_concept_eurovoc (EuroVoc subject), cdm:work_cites_work (citation). Virtuoso does not support bif:contains for multi-word phrases; use FILTER(CONTAINS(LCASE(?title), "keyword")).
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  • Run a System of Record adjudication on an entity surfaced by an AI engine (e.g. is 'Banner Life' a valid PMI competitor to Enact?). Uses dual-model consensus (Haiku 4.5 + Gemini Flash, escalating to Sonnet 4.6 + Gemini Pro on disagreement) against a versioned taxonomy. Returns the Why Drawer headline, audit trail, and per-model judgments. Pro plan or higher required.
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  • Given a Weibull shape parameter β (and optionally the characteristic-life parameter η), return a plain-language interpretation: which bathtub-curve regime β implies (infant mortality / random / wearout), what action that suggests (process-of-care / steady-state monitoring / maintenance scheduling), and — if η provided — closed-form MTTF and B-life numbers from the Weibull formulas. Pure-math + lookup, no engine call, fully deterministic. Use when a user reports a fitted β and wants to know what to DO with it. ANTI-FABRICATION: MTTF and B-life are exact closed-form values from the two-parameter Weibull (η · Γ(1+1/β) and η · (-ln(1-p))^(1/β)). Quote them verbatim.
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  • Export project data in JSON-LD format using a configured export profile. Use `fetch_json_ld_import_profiles` first to discover the configTypeId. At least one of exportTypes/exportElements/exportTaxonomies/exportDataTypes must be true. Tree-based mode (graphBased=false) requires exactly one nodeId; graph-based mode allows multiple or none.
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  • Get a compact summary of a Figma design file by its file key: name, last modified time, version, editor type, role, thumbnail URL, and the list of top-level pages (id, name, type, child count). Use to understand the structure of a Figma design file without fetching the full node tree.
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  • Generate design systems: OKLCH color palettes, fluid type scales, spacing, shape and icon tokens.

  • The industry standard reference for safe, observable, and steerable AI agent UX. Browse and search the 10 Blueprint principles, principle clusters, curated implementation examples, and application guides. 13 public tools require no credentials. Tools for learning path, coaching context, and handoffs require a Firebase Bearer token. Validation and usage summary tools require a Pro or Teams membership.

  • Search MyDisease.info for diseases by free-text name or fielded query. Returns matching hits, each keyed by a MONDO disease id (e.g. "MONDO:0015967") with the best-matching ontology and annotation keys. Use this to resolve a disease name to canonical ontology ids before calling the "disease" tool. Free text like "diabetes" or "asthma" works; fielded queries like "mondo.label:asthma" or "disgenet.xrefs.disease_name:..." narrow the search.
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  • Resolve scientific (Latin binomial) names to Open Tree of Life taxon IDs (ott_id). The ott_id is the key needed by taxon_info and common_ancestor — start here. Returns the best match per name with its ott_id, accepted name, rank, and synonym/approximate flags. Keyless.
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  • Find the most recent common ancestor (MRCA) of 2-10 taxa in the synthetic Tree of Life, by ott_id (from match_names). Returns the common-ancestor taxon (ott_id, name, rank). Keyless.
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  • Calculate a Destiny Matrix (also known as "Matrix of Destiny" or numerology matrix) from a single date of birth. Returns a structured numerology reading: core numbers (1–22) for personality, talents, money, relationships, life purpose, and inherited/karmic patterns, each with a short text interpretation. Use this when a user asks to compute, build, or interpret a destiny matrix, or wants a numerology personality reading based on a birth date.
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  • Returns AdCritter design guidance for an entity at a caller-chosen guidance level - screen experiences, API integration patterns, and design philosophy. The default ('full') returns step-by-step prescription (exact layouts, colors, copy text, column orders). Request 'patterns' for balanced hints including common design patterns with softened vocabulary. Request 'facts' if you have strong visual-design instincts and just want API integration bindings (or call adcritter_get_api_reference and adcritter_get_usage_guide directly and skip this tool). Guidance is format-agnostic - it describes outcomes and integration, never prescribes frameworks or architecture. Available entities: ad, advertiser, audience, authentication, blueprint, campaign, geo, media-asset, plan, report, settings.
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  • Fetch a single Catalogue of Life name-usage record by ID (includes taxon, name, status, and source dataset). Use after `search` or `name_match` to get the full structured record; for the taxonomy chain use `classification`, for common names use `vernacular`.
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  • Auto Captions — Automatically add subtitles to video using AI speech recognition. AI Studio run — dispatches to our AI workers (Modal). Credits per run vary by model and file size. Day Pass and welcome credits do not include AI Studio. Files auto-delete within 24 hours; retention is auditable at mioffice.ai/account/tasks. All three credit-based workspaces unlock with the same one-time credit pack — there is no per-workspace subscription. See mioffice.ai/pricing for current plans.
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  • Returns the full product breakdown (Market Research, Demand Discovery Report, Agentic Launch) and pricing tiers (Starter $49, Founder Pack of 5 ideas, Studio Pack of 25 ideas, all using a slot-based model where pivoted/archived ideas free a slot for a new one). Use when a user asks "what does Demand Discovery AI include?", "how much does it cost?", "what's in the report?", or wants concrete product information. Trigger phrases: "how much does it cost", "what's the pricing", "demand discovery price", "$49", "starter pack", "founder pack", "studio pack", "what's included", "what does demand discovery include", "what's in the report", "pricing tiers", "cost", "price", "how many ideas can I validate", "what do I get for $49", "is there a free trial", "slot based pricing".
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  • Search the web using Bing. Returns organic results, related searches and more. Alternative to Google for web search with different ranking algorithms and results.
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  • Search the Autario public data catalog. Returns dataset IDs, titles, descriptions, categories, publishers, row counts, last_refreshed_at, AND trusted ontology fields (topic, subtopic, unit, frequency, entity_type, indicator_id) when ontology confidence is high. Use this first to discover available datasets before querying. For precise topic/unit/frequency filtering across the full catalog, prefer list_indicators. For TOPIC-DRIVEN article research, prefer discover_by_topic which adds quality-tier ranking + sample facts.
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  • "What species are in [genus]" / "all members of [family]" / "species under [order]" / "direct descendants of [taxon]" — list immediate child taxa under a Catalogue of Life parent taxon. Use to walk down the tree of life one rank at a time.
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  • "What is the scientific name of [animal/plant]" / "what family does [species] belong to" / "look up [organism] taxonomy" / "find [genus/family] in the tree of life" / "common name → Latin binomial" / "is [name] a real species" — AUTHORITATIVE taxonomic lookup for any species, genus, family, or higher taxon. Source: Catalogue of Life — the global consensus checklist of every known living organism (~2.1M accepted species names plus synonyms). Returns matching name-usages with scientific name, authorship, rank (kingdom→species), status (accepted | synonym | misapplied), and taxon ID for follow-up calls to `taxon` or `classification`. Filter by `rank` ("species" / "genus" / etc.) when narrowing.
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  • # Tool: WixREADME **Directive:** `WixREADME` is the **MANDATORY FIRST STEP** for all Wix-related tasks. Its output (including relevant linked documents) provides foundational context for all other Wix tools. Adherence to this protocol is **NON-NEGOTIABLE**. <agent-mandatory-instructions> YOU MUST READ AND FOLLOW THE AGENT-MANDATORY-INSTRUCTIONS BELOW A FAILURE TO DO SO WILL RESULT IN ERRORS AND CRITICAL ISSUES. <goal> You are an agent that helps the user manage their Wix site. Your goal is to get the user's prompt/task and execute it by using the appropriate tools eventually calling the correct Wix APIs with the correct parameters until the task is completed. </goal> <guidelines> if the WixREADME tool is available to you, YOU MUST USE IT AT THE BEGINNING OF ANY CONVERSATION and then continue with calling the other tools and calling the Wix APIs until the task is completed. **Exception:** If the user asks to create, build, or generate a new Wix site/website, skip WixREADME and: - If the user **explicitly** mentions a template, Wix Studio, or headless → call CreateWixBusinessGuide directly. - Otherwise → call the WixSiteBuilder tool directly. **Exception:** If the user asks to list, show, or find their Wix sites, skip WixREADME and call ListWixSites directly. **Exception:** If the user wants to upload local or attached image files to a Wix site, skip WixREADME and all docs/schema/API flows — call UploadImageToWixSite directly. Do NOT use ExecuteWixAPI, SearchWixAPISpec, or any Media Manager REST API for image uploads. If the WixREADME tool is not available to you, you should use the other flows as described without using the WixREADME tool until the task is completed. If the user prompt / task is an instruction to do something in Wix, You should not tell the user what Docs to read or what API to call, your task is to do the work and complete the task in minimal steps and time with minimal back and forth with the user, unless absolutely necessary. </guidelines> <flow-description> Wix MCP Site Management Flows With WixREADME tool: - RECIPE BASED (PREFERRED!): WixREADME() -> find relevant recipe for the user's prompt/task -> read recipe using ReadFullDocsArticle() -> call Wix API using CallWixSiteAPI() based on the recipe - CONVERSATION CONTEXT BASED: find relevant docs article or API example for the user's prompt/task in the conversation context -> call API using CallWixSiteAPI() based on the docs article or API example - EXAMPLE BASED: WixREADME() -> no relevant recipe found for user's prompt/task -> BrowseWixRESTDocsMenu() or SearchWixRESTDocumentation() -> find relevant method -> read method article using ReadFullDocsArticle() to get method code examples -> call API using CallWixSiteAPI() based on the method code examples - SCHEMA BASED, FALLBACK: WixREADME() -> no relevant recipe found for user's prompt/task -> BrowseWixRESTDocsMenu() or SearchWixRESTDocumentation() -> find relevant method -> read method article using ReadFullDocsArticle() -> no method code examples found -> inspect the method schema using SearchWixAPISpec or ReadFullDocsMethodSchema -> call API using CallWixSiteAPI() based on the schema Without WixREADME tool: - CONVERSATION CONTEXT BASED: find relevant docs article or API example for the user's prompt/task in the conversation context -> call API using CallWixSiteAPI() based on the docs article or API example - METHOD CODE EXAMPLE BASED: BrowseWixRESTDocsMenu() or SearchWixRESTDocumentation() -> find relevant method -> read method article using ReadFullDocsArticle() to get method code examples -> call API using CallWixSiteAPI() based on the method code examples - FULL SCHEMA BASED: BrowseWixRESTDocsMenu() or SearchWixRESTDocumentation() -> find relevant method -> read method article using ReadFullDocsArticle() -> no method code examples found -> inspect the method schema using SearchWixAPISpec or ReadFullDocsMethodSchema -> call API using CallWixSiteAPI() based on the schema </flow-description> </agent-mandatory-instructions>
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