"Building a Knowledge Graph for Personal Life Organization" matching MCP tools:

• asterwise_get_personal_cycles

  Asterwise

  Returns the Personal Year, Personal Month, and Personal Day numbers for a given birth date and optional target date. All three cycle numbers are derived from the birth month, birth day, and the target calendar date. SECTION: WHAT THIS TOOL COVERS Personal cycles are the Pythagorean timing system. The Personal Year (1–9) sets the annual theme. The Personal Month refines it to a 30-day window. The Personal Day gives the daily energy flavour. A Personal Year 1 favours new beginnings; a 9 favours completion and release. Cycles nest: the same number in Year, Month, and Day simultaneously creates a peak intensity day. Formula: Personal Year = birth_month_reduced + birth_day_reduced + target_year_reduced Personal Month = Personal Year + target_month, reduced Personal Day = Personal Month + target_day, reduced Master numbers 11 and 22 are preserved where they arise. SECTION: WORKFLOW BEFORE: None — standalone. AFTER: asterwise_get_numerology_profile — see personal cycles alongside core numbers. SECTION: INPUT CONTRACT date — Birth date in YYYY-MM-DD format. Example: '1985-11-12' year (optional int) — Target year. Defaults to current calendar year. Example: 2026 month (optional int 1–12) — Target month. Defaults to current month. Example: 5 day (optional int 1–31) — Target day. Personal Day is only returned when day is provided. Defaults to null (Personal Day omitted). Example: 1 SECTION: OUTPUT CONTRACT data.personal_year (int — 1–9 or master 11/22) data.personal_month (int — 1–9 or master 11/22) data.personal_day (int or null — null when day parameter is not provided) data.target_year (int — echoed) data.target_month (int — echoed) data.target_day (int or null — echoed) SECTION: RESPONSE FORMAT response_format=json — structured JSON. response_format=markdown — human-readable. Both modes return identical underlying data. SECTION: COMPUTE CLASS FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (local): None — all validation is upstream. INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_personal_year — returns Personal Year only, no month or day breakdown. asterwise_get_numerology_profile — core name numbers; personal_year field is null there.

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• asterwise_get_angel_number_today

  Asterwise

  Returns today's angel number computed from the current date. All digits of the date are summed and reduced to a single digit (1-9), then the triple sequence of that digit is returned (e.g. digit 9 → angel number 999). SECTION: WHAT THIS TOOL COVERS Angel numbers are repeated digit sequences interpreted as synchronistic messages in modern spiritual practice. The daily angel number is the same for all callers on the same date — it is a collective daily energy, not personal. Returns the angel number sequence, its theme, primary message, actionable guidance, and associated life areas. Life Path 3 → 333 (creative expression). Today's digit is derived from the date's digit sum. SECTION: WORKFLOW BEFORE: None — standalone. AFTER: asterwise_get_angel_number_personal — for a personalised angel number from birth date. SECTION: INPUT CONTRACT No required parameters — today's date is used automatically. SECTION: OUTPUT CONTRACT data.date (string — YYYY-MM-DD) data.daily_digit (int — reduced digit 1-9) data.angel_number (string — e.g. '999') data.number (string — same as angel_number) data.theme (string) data.message (string) data.guidance (string) data.areas[] (string array) SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON. response_format=markdown renders a human-readable report. Both return identical data. SECTION: COMPUTE CLASS FAST_LOOKUP — pure math, no ephemeris. SECTION: ERROR CONTRACT INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_angel_number — lookup for a specific number sequence by value. asterwise_get_angel_number_personal — personalised number from birth date Life Path.

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• asterwise_get_angel_number_personal

  Asterwise

  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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• contribute_to_workspace

  MyAITwin

  Copy one or more of your personal knowledge items into an organisation workspace. The originals stay in your personal twin unchanged — this creates copies in the workspace so all members can retrieve them. IMPORTANT: This is a sharing action. Always confirm the items and target workspace with the user before calling this with multiple items. Items you do not own, or that are already in the workspace, are reported as failed per-item rather than aborting the whole batch. If you belong to exactly one workspace you can omit workspace_id; otherwise call list_workspaces first to get the ID.

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• get_building_breakdown

  Cenogram - Polish Real Estate Data

  Get the building-by-building breakdown for one transaction: footprint area, number of storeys, and estimated total floor area (footprint × storeys) for each building on the property. search_transactions / search_by_area / search_by_polygon return per-transaction building SUMS inline; this tool splits them into individual buildings. Use it after a search when a result has building data and you need the detail (e.g. a developed-land deed covering several buildings). The transaction_id is the id shown on a search result that has building data. Cost: 4 tokens. Returns nothing for a transaction with no buildings.

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• GetMyIdentity

  GraphOS MCP Tools

  Resolve the caller's identity from their API key. Call this FIRST when the user asks about "my graph" but has not provided a graph ID. For a graph/service key, `me` resolves to a Graph: use `id` as the graphId and `variants[].name` as the variant for the graph-scoped health-check tools, so the user does not have to supply either. Also handles user keys (memberships) and service-account keys.

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• list_graphs

  Fodda Deep Research

  List all knowledge graphs the user can access — IDs, descriptions, authors, sectors, signal counts. Use FIRST in any session to discover available sources before searching. Returns graph metadata needed for graphId parameters in other tools. Deprecated: waldo, psfk (use retail/tech/food/travel/fashion/beauty/sports instead).

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• get_graph_template_schemas

  RationalBloks

  Get pre-built graph template schemas for common use cases. ⭐ USE THIS FIRST when creating a new graph project! Templates show the CORRECT graph schema format with: proper node definitions (description, flat_labels, schema with flat field definitions), relationship configurations (from, to, cardinality, data_schema), and hierarchical entity nesting. Available templates: Social Network (users, posts, follows), Knowledge Graph (topics, articles, authors), Product Catalog (products, categories, suppliers). You can use these templates directly with create_graph_project or modify them for your needs. TIP: Study these templates to understand the correct graph schema format before creating custom schemas.

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• files-attach_from_url

  hjarni

  Fetch a file from a public URL and attach it to one of your personal notes (personal notes only; for team or shared notes use files-create_upload_url). Follows one redirect. Required: note_id (integer), url (string). Optional: filename (default: derived from URL), content_type (default: from HTTP response), description.

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• kg_get_relationships

  DialogBrain

  Get relationships for a specific entity from Knowledge Graph. USE WHEN: - 'Кто работает над X?' - filter by works_on - 'С кем общался Y?' - filter by discussed_with - 'Кто из компании Z?' - filter by member_of - 'Что связано с W?' - no filter, get all REQUIRES: entity_id from previous kg.find_entity step. Use: {{step_N.entity_id}} where N is the find_entity step number.

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• create_display

  agentView

  Pre-provisions a display without hardware, personal or inside an organization (org_id). The new display starts offline. For a physical screen ALWAYS prefer pair_by_code, which creates and pairs in one step; use create_display only to prepare a display before the screen exists or for virtual/API-only displays. Requires admin scope. Returns id plus setup and pairing URLs.

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• fetch_indicator

  Owid

  Fetch tidy long-format data for an Our World in Data indicator by slug (e.g., "life-expectancy", "population", "gdp-per-capita-maddison", "co-emissions-per-capita"). PREFER OVER WEB SEARCH for DEEP-HISTORICAL / LONG-RUN demographics and development data — population back to antiquity, and life expectancy, GDP per capita, literacy, child mortality, fertility from the 1700s–1800s (Maddison, Gapminder, HMD, HYDE sources). Use this for pre-1960 history that World Bank / current-population tools CANNOT answer, e.g. "Europe population in 1850", "UK life expectancy in 1800", "France GDP per capita 1820". Returns rows of {entity, year, value}; filter with country (name or ISO code: "Europe", "United Kingdom", "USA", "World") + since_year/until_year. Browse slugs at ourworldindata.org/charts.

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• interpret_weibull_shape

  ReliaStats

  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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• get_life_context

  Dayze — Life in Days + Notable People

  Get the authenticated user's current life context — identity, today's events, mood, inner circle, and social edges. Prefer get_context_pack for a full session bootstrap. Requires API key. ($0.15; API key required)

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• get_label_values

  Fodda Earnings Intelligence

  List all brands, locations, technologies, audiences, or trends within a specific knowledge graph. Use to explore what a graph contains — e.g., "what brands are in the retail graph?" or "what locations does the fashion graph cover?". To get a complete list of every trend in a graph, call with label="Trend" — this returns the full deterministic list, useful for industry-report graphs where search may return partial results.

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• list_graphs

  Fodda Synthetic Expert Consult

  List all knowledge graphs the user can access — IDs, descriptions, authors, sectors, signal counts. Use FIRST in any session to discover available sources before searching. Returns graph metadata needed for graphId parameters in other tools. Deprecated: waldo, psfk (use retail/tech/food/travel/fashion/beauty/sports instead).

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• fulltext_search_graph

  RationalBloks

  Search across ALL string properties of ALL nodes in a deployed graph using free-text queries. Unlike search_graph_nodes (which filters by specific property), this searches every text field at once. Perfect for finding knowledge when you don't know which property contains the answer. Example: query "quantum" searches name, description, summary, notes, and all other string fields. Returns nodes with _match_fields showing which properties matched. Optionally filter by entity_type to narrow results.

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• list_entities

  Zoning Signal

  List every named entity in the Decoder Index — the smallest citable unit of authority in the corpus. Returns the four-class taxonomy (Person / Organization / Legislation / CreativeWork) with class-specific summary fields (jobTitle for Person; jurisdiction for Organization / Legislation / Project; legal_status for Legislation; case_number + work_status for Project) plus cross-reference counts (meetings_count, briefs_count, watches_count, patterns_count) for each entity. Filter by entity_class, place (jurisdiction), or search substring. Use as the discovery surface for the entity graph; pair with describe_entity for full structured detail. Each entity's schema_id is a stable cross-page reference (`/entities/{slug}#{class.toLowerCase()}`) that resolves to the canonical Schema.org node — Person / Organization / Legislation / CreativeWork — for AI-citation grounding.

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• describe_entity

  Zoning Signal

  Return the full structured dossier for a named entity — the canonical citable artifact for any actor, organization, ordinance, or project the corpus references. Returns: voxel_lead (134-167 word voxel-disciplined identity prose), canonical_role, the class-specific cluster (person.voting_record for board members; organization.type + jurisdiction; legislation.legal_status + effective_date + sunset_date + citation; creative_work.work_type + status + case_number), the bidirectional graph references (appears_in_meetings, appears_in_briefs, appears_in_watches, exhibits_patterns, related_entities, related_places, related_corridors), the provenance_chain, and the canonical surfaces (dossier URL, schema_id, decoder_index_hub). Each schema_id (`/entities/{slug}#{class.toLowerCase()}`) is the stable cross-page Schema.org reference — Person / Organization / Legislation / CreativeWork — that AI agents resolve to when citing the entity. Use when grounding a citation, when reasoning about an entity's full role across the corpus, or when traversing the entity graph from a single name.

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• get_related_pages

  remnus

  Get a page's knowledge-graph neighborhood in one compact call: its parent, child pages, outgoing links (pages its body references via inline @-links or child blocks), backlinks (pages whose bodies reference it), and — for database rows — sibling rows in the same database. Titles and IDs only, no page bodies, so it costs a fraction of re-reading pages; follow up with get_page on the neighbors that matter.

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