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619,814 tools. Updated 2026-09-28 18:28

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

  • Generate a video post (postType VIDEO) using PostNitro's AI engine. Returns an embedPostId to track progress. The content is AI-generated from your aiGeneration prompt — you do NOT provide slides. Each slide the AI writes becomes a scene in the video. (To supply your own scene content instead, use postnitro_import_video.) Output: 'DESIGN' (default) creates the design without rendering; 'MP4' renders the video file and requires videoSettings (duration, optional audio track). templateId, brandId, presetId, and responseType are optional if you've saved defaults via postnitro_set_defaults. Otherwise provide them here (use the list tools to find valid IDs). Use postnitro_check_status to monitor, then postnitro_get_output to retrieve. Or use postnitro_generate_video_and_wait for one step.
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  • 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. WORKFLOW: BEFORE: RECOMMENDED — asterwise_get_numerology_profile — confirm Life Path before calling. AFTER: None. INPUT CONTRACT: date: Birth date in YYYY-MM-DD format. Example: '1994-03-31' name (optional): Person's name for personalisation. 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. Full output and error contract: https://docs.asterwise.com/mcp/tools/get-angel-number-personal/
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  • 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. WORKFLOW: BEFORE: RECOMMENDED — asterwise_get_numerology_profile — confirm Life Path before calling. AFTER: None. INPUT CONTRACT: date: Birth date in YYYY-MM-DD format. Example: '1994-03-31' name (optional): Person's name for personalisation. 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. Full output and error contract: https://docs.asterwise.com/mcp/tools/get-angel-number-personal/
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  • Casts a short thought into sendiment (https://benjaminmower.github.io/sendiment/), a public river of ephemeral thoughts. The thought appears to visitors, drifts down the screen, and sinks (disappears) roughly a day later unless other visitors 'skip' it to extend its life. Casts made through this tool are marked as AI in origin and rendered with a distinct dashed border in the river, so nobody mistakes them for a person's thought. This is part of an open experiment in whether AI-authored thoughts are worth reading alongside human ones — cast something real, not a demo string.
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  • Return the curated list of example quantum algorithms with published resource estimates (qubit count, depth/gate count, source paper URL). Useful for comparing what algorithms need vs. what hardware can deliver. Each entry carries a `provenance` field: 'published-circuit' means the figure is reproducible from the source, 'attested-estimate' means the source withholds the circuit and the figure rests on the authors' attestation, with a `provenanceNote` giving the specifics. Carry that caveat whenever you quote an attested figure; do not present it as equivalently sourced.
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  • AI Document Translator — Translate text between 16 languages using AI. 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 are deleted after processing; auditable at mioffice.ai/account/tasks (retention details at mioffice.ai/privacy). 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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Matching MCP Servers

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    license
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    quality
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    maintenance
    Enables checking project files like Dockerfile, .nvmrc, and CI workflows to determine whether pinned runtime, database, and OS versions are still supported, providing end-of-life dates, latest patches, and upgrade targets for 470+ products.
    3
    MIT
  • F
    license
    Not graded
    quality
    C
    maintenance
    Read-only MCP server for the July 2026 survey of AI in open-source design systems, enabling agents to query 19 systems' affordances, coercion techniques, and platform data via 9 tools, 2 resources, and 2 prompts.
    4
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Matching MCP Connectors

  • Create and search memories in your personal life constellation, UluP Life.

  • Guide user-led frontend design decisions from project brief through implementation review.

  • Create an empty diagram and return its diagram_id, so you can build it yourself with the canvas_* tools. No AI generation runs: you draw it. Use this when you want control over the result, or already know the architecture from the conversation. Use create_diagram instead when you want Datadef's model to design the whole thing from a prompt. Call get_design_guide first: it is the standard Datadef's own generator follows, and building without it produces diagrams this tool exists to avoid.
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  • Upload a photo to a Life Graph entity. Accepts image_base64 or url (one required). Re-encodes via WebP (strips EXIF/AI metadata). Person uploads also write the CRM gallery (person_photos) and avatar when set_as_avatar=true. ($0.15; API key required)
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  • Find observation variables (traits) by name, trait class, ontology term, or free-text query. Free-text queries are ranked against the returned set and may resolve to ontology URIs when the server advertises them. When the upstream total exceeds loadLimit, the full result set is materialized as a dataframe — query it with brapi_dataframe_query (SQL).
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  • Fetch the full aggregated annotation object for a single disease id. Accepts MONDO ("MONDO:0015967"), DOID ("DOID:9351"), OMIM ("OMIM:125853") and other supported ontology ids. Returns cross-referenced data including MONDO ontology (labels, synonyms, xrefs, parents/children), gene-disease associations from DisGeNET, phenotypes from HPO, and chemical-disease relationships from CTD. Resolve a name to an id first via the "query" tool.
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  • Deterministic check of one appointment ask on a sales call using six fixed text-pattern rules: day, clock time, holding the time, confirmation question, naming what the customer asked about or promising it ready, and follow-up. Returns a structured score, per-part flags and fixes, and a fixed template example that reuses a detected day and time or supplies sample values when absent. Runs without an AI model. Use this for repeatable rule-based scoring; use review_appointment_ask for contextual AI feedback and a tailored rewrite.
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  • Deterministic check of one appointment ask on a sales call using six fixed text-pattern rules: day, clock time, holding the time, confirmation question, naming what the customer asked about or promising it ready, and follow-up. Returns a structured score, per-part flags and fixes, and a fixed template example that reuses a detected day and time or supplies sample values when absent. Runs without an AI model. Use this for repeatable rule-based scoring; use review_appointment_ask for contextual AI feedback and a tailored rewrite.
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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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  • Score a URL for design-system AI readiness, the 6th maturity axis (zeroheight 2026). 10 checks probe the target origin for machine-readable artifacts: DTCG token files, llms.txt, agent.json, MCP endpoint (tools/list), DESIGN.md, token $description, component schemas, sitemap.xml, robots.txt, and Open Graph/Twitter meta. Use this to verify whether a design system is the default context AI tools build from, or whether AI is silently working around it. When NOT to use: for full design-contract scoring, use designesy_score; for AI-drift detection, use designesy_drift_score. Executable: fetches the URL and probes the origin via HEAD/GET for each artifact. No browser needed. Returns JSON: { ok, url, score (0-100), grade (A-F), pass, warn, fail, total, checks[{id, item, category, status, detail}] }. Results cached ~24h per URL.
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  • Search financial data and return an answer with an inline widget. Covers stocks, macro, forex, crypto, commodities, earnings, technicals, and related market questions. Pass the user's question in `query` without rephrasing. To screen a named watchlist or portfolio, call list_watchlists or list_portfolios first, then pass the symbols here with filter criteria in `query`. Exclusive modes when a Deep Dive exists: focus=ai + section=… → narrative section focus=raw + source=… → thin data slice focus=catalog → list available Deep Dives Args: query: The user's financial question symbols: Optional ticker universe to screen (e.g. from list_watchlists) section: Optional Deep Dive section (competition, valuation, thesis, forecast, analyst_consensus, people, financials, earnings, technicals, overview, moves, markets, flow, calendar, catalysts, odds) or raw tapes: options, buzz, acceleration, heatmap. Buzz, social media, reddit, twitter, sentiment, or what people are talking about MUST be section=buzz. Do not send those to news or catalysts. Consumer sentiment and analyst sentiment are not Buzz. Stocks accelerating, price acceleration, speeding up, or speeding down MUST be section=acceleration. Do not send those to movers. Revenue, earnings, and growth acceleration are not this board. Advance/decline, names within 10% of the 52-week high or low, or the market P/S, P/E, and P/FCF medians MUST be section=heatmap. Do not send those to the sector treemap. One company's P/E is not this row. focus: Optional exclusive mode: ai | raw | auto | catalog source: Optional thin raw slice when focus=raw (comparison_peers, price_targets, consensus, quote, key_financials, …)
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  • Returns a 0-100 US consumer-loan delinquency stress score (z-scored composite of EIA consumer-survey and utility-payment stress indicators, history to 1988) with stress_score, acceleration, percentile_rank, confidence, and methodology_version. Call when the user asks about consumer credit stress, delinquency trends, or eviction risk, or when timing collections staffing, bad-debt provisioning, or rental-portfolio exposure — acceleration leads residential eviction filings by about two months. Updates: quarterly.
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  • Face Swap — Swap faces between photos using AI. 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 are deleted after processing; auditable at mioffice.ai/account/tasks (retention details at mioffice.ai/privacy). 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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  • AI Document Summarizer — Summarize long documents into key points using AI. 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 are deleted after processing; auditable at mioffice.ai/account/tasks (retention details at mioffice.ai/privacy). 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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  • AI Clip Maker — Extract the best short clips from long videos using AI. 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 are deleted after processing; auditable at mioffice.ai/account/tasks (retention details at mioffice.ai/privacy). 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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  • Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for true semantic similarity with vector embeddings, use run_semantic_tests with mode="embeddings" and your OpenAI API key. Supports single pair or batch mode with pipe-separated pairs. Useful for RAG retrieval testing, semantic search evaluation, and text deduplication.
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  • "What does HP:[N] mean" / "look up HPO phenotype [ID]" — fetch a single Human Phenotype Ontology (HPO) term by ID. HPO is the standard ontology for clinical phenotypes used in rare-disease research. Returns label, definition, synonyms, cross-references. Example ID: HP:0001250 (Seizure).
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