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742,684 tools. Updated 2026-11-04 17:14

"The concept of thinking or introspection" matching MCP tools:

  • Get Venture Insights' live service catalogue: the FREE Concept Diagnostic (a research-backed viability study of one venture concept, delivered to the founder's inbox) and the paid study tiers with live SAR prices. Call this first when your user asks what Venture Insights offers, what it costs, or whether the free diagnostic is worth requesting.
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  • Add an identity concept at zero usage, or add text as an alias using alias_of. Requires editor; resolution may incur embedding/judge cost. Probe first; if the text already resolves to an incumbent, offer that concept instead of blindly retrying. Aliases resolving to another concept are refused. embedding_model selects a new type's space only. Returns concept details and link; manage existing aliases with update_concept_alias. See enricher://docs/semantic-ids.
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  • Search the recipe collection semantically (dense + ColBERT hybrid) to gather the members of a theme. Pass a rich natural-language concept, not just a keyword — e.g. 'cozy cold-weather baked desserts', 'bright summer salads with fresh vegetables', 'Latin American and Caribbean mains', 'anything featuring apples'. Returns up to `limit` compact hits {id,title,cuisine,dietary,cookbook}. Call once per theme concept; call again with a broader/narrower query if a group is too small or too big. Do NOT rely on cuisine labels alone — many recipes are unlabelled and only reachable by concept search.
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  • Start a qualitative research study: AI-moderated in-depth interviews (voice, video, or chat), concept tests (images of packaging, claims, ad creative, or designs), or prototype tests (clickable prototypes, websites, landing pages). Use when the user wants to run a new interview study on User Intuition (e.g. churn, win-loss, brand switching, path to purchase, onboarding). Not for multiple-choice surveys. Creates an in-depth-interview, concept-test, or prototype-test draft using ordinary metadata only. Ask the human to explicitly choose recruiting_method: panel (User Intuition recruits real participants), BYOP (bring your own participants, invited by email), or synthetic respondents. A value proposed or inserted by a host/delegating model is not human confirmation unless it quotes the human's actual choice. Unless the human requests otherwise, use a voice interview in English with the male interviewer voice; these defaults are applied when interview_format, language, or voice are omitted. Synthetic respondents require chat. A prototype-test must use video or voice, never chat. Do not draft or pass the study plan, audience targeting, screener questions, concept links, or concept images here; after creation, send the user's natural-language research brief to customize_study. For a concept test, pass an accessible attachment through customize_study.concept_image or include a publicly downloadable concept-image URL in the message, plus its participant-facing label, stimulus context, audience, and learning goals. For a prototype test, include the prototype URL, participant-facing label, intended tasks, audience, and learning goals. customize_study validates and attaches the asset. Voice configuration is kept for chat as well as voice and video.
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  • Lists the raumnebenan content categories (pillars of Product Thinking) with their title and a scope description, as maintained in the CMS. Use this tool to decide which category fits the user's question, then call get_stories_by_category with the matching category value.
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  • Search the EuroVoc thesaurus, resolving a keyword into concept URIs usable in the eurovoc_concept subject filter of eurlex_search_documents. CELLAR indexes no judgment, order, or Advocate General opinion of the Court of Justice or General Court by EuroVoc concept; only EFTA Court judgments carry one, so search case law by subject with eurlex_get_cases subject. Matches both preferred and alternative (non-preferred) labels, so a common synonym reaches the concept it stands for. Returns each concept URI, its preferred label in the requested language, code, broader (parent) label, and the alternative label that matched when one did. Concepts with an exact label match come first, then those whose label or one of its words starts with the keyword, then other substring matches, each group ordered by preferred label.
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Matching MCP Servers

  • A
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    Enables LLMs to perform conceptual search over local PDF/EPUB documents using a RAG pipeline with corpus-driven concept extraction and WordNet enrichment.
    3
    MIT
  • A
    license
    Not graded
    quality
    D
    maintenance
    Concept-test your product with synthetic consumers, straight from Claude Code. It turns free-text reactions from roleplayed personas into purchase-intent reports using Semantic Similarity Rating.
    2
    MIT

Matching MCP Connectors

  • Find relevant Smart‑Thinking memories fast. Fetch full entries by ID to get complete context. Spee…

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  • Build a company financial profile in one call: the latest value of every supported XBRL concept, grouped by statement. Reads the filer's complete companyfacts payload once rather than one request per concept, so it replaces a run of secedgar_get_financials calls when the question is "what do this company's financials look like right now". Values use the same frame dedup and tag priority as secedgar_get_financials, so the two agree for any concept they both cover. Duration concepts (income statement, cash flow, per-share) report their latest full year and latest single quarter; balance-sheet and entity-info concepts report their latest point-in-time value, since that is the only form they are filed in. A concept the filer does not report is listed under gaps with the XBRL tags that were tried — never zero-filled or interpolated. Use secedgar_get_financials for a full time series of one concept, and secedgar_compare_companies to put several companies side by side.
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  • Show the founder an interactive intake form to start their FREE Concept Diagnostic. PREFER calling this over asking for the founder's name, email and concept one message at a time — it collects everything in one card and starts the diagnostic on submit. Call it as soon as the user wants to start, or check the viability of, an idea. The form is deliberately collected FRESH from the founder and starts BLANK — it does NOT accept or pre-populate remembered details, so the founder always enters (and sees) their own name, email and concept. This keeps the destination email accurate (one free diagnostic per founder, emailed to the address they type). Takes no arguments.
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  • The full XBRL-to-JSON converter. By default returns a company's normalized income statement, balance sheet and cash flow across every reported period, plus a concept index of every us-gaap/dei tag it reports. Pass `concept` to pull the raw time series for any specific tag(s) beyond the curated fundamentals; pass `accession` for every concept reported in one specific filing.
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  • The full XBRL-to-JSON converter. By default returns a company's normalized income statement, balance sheet and cash flow across every reported period, plus a concept index of every us-gaap/dei tag it reports. Pass `concept` to pull the raw time series for any specific tag(s) beyond the curated fundamentals; pass `accession` for every concept reported in one specific filing.
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  • Return a single-call snapshot of this user's Upfixe state — email, admin flag, the Google Ads principal/MCC they pinned, brand count + plan limit, drafts/published campaign counts, reports count, plan tier, and trial days remaining. Use this when the user asks 'what's my MCC?', 'which plan am I on?', 'how many brands do I have?', or any other introspection question — it's a Supabase-only read so it's instant. Don't call list_ad_accounts (slow, Google API) just to answer 'what's my MCC?'.
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  • Preview identity resolution without adding a concept or increasing its usage. Requires editor; uncached resolution may call embeddings and the identity judge. Returns exact_hit, match or no_match, the matched concept and neighbors. Probe before adding; a matched incumbent may already represent the intended entity. embedding_model can select the space for a new concept type, not change an existing type's space. Inspect judge evidence as well as similarity. See enricher://docs/semantic-ids.
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  • Evaluates whether abstractions provide a genuinely different way of thinking or are structurally shallow. Use when adjacent layers feel redundant, wrappers add boilerplate without depth, or an abstraction feels leaky. Not for a single module's interface-to-implementation ratio (use deep-modules) or information leakage across boundaries (use information-hiding).
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  • Generic concept → theme pipeline for a Game Kit project: the saved art direction (gripforge_game_art_direction: look, theme, palette, notes, device) becomes ONE interface concept image (title, main menu, HUD with health, a weapon card, key prompts), a vision pass reads its design tokens (colour roles, radius, border width, button shape, padding, letter case, weight, the closest open-licence font, glyph style, closest preset) and they are written as the ui.theme config (contrast repaired). The concept is kept in the Library (kind concept, category ui). action=quote prices it: 1 credit per run (quote it and ask the user before run). Without regenerate the latest concept of the project is reused with the tokens already read from it (0 credits), so a run after a dry run applies exactly what the dry run showed; reread=true reads the concept again (1 credit). dry_run=true writes nothing. run returns a generation job: poll it with gripforge_generation_read; its result carries config, tokens, fixes, previous_config and revision_before for a rollback (gripforge_gamekit_configure ui.theme with previous_config).
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  • Single-regime introspection: returns the median behavioural descriptors of a known regime, the z-scores vs the catalogue population (so you can see what makes THIS regime distinct from the average), an English characterisation generated from the most extreme descriptors, and the top 2 nearest neighbours as a preview. Complements find_similar_regime: that tool ranks neighbours of a target, this tool tells you what a single regime IS. Read this before searching if you want to reason about one regime first.
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  • Resolves a name — a company, product, person, technology, or concept — to its Google Trends topic id (`mid`), with a `type` field that distinguishes same-name entities such as Nike the company from Nike the goddess. A topic aggregates every spelling and translation of one concept, so it measures considerably more search activity than a literal phrase: the topic for "artificial intelligence" scores 62 where the literal string scores 1. The other TrendFlow tools accept a topic id anywhere they accept a keyword.
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  • Run a GraphQL introspection query to discover the project's schema — all types and fields, including the available flags and the shape of the evaluation context. Best-effort: some edge deployments disable introspection, in which case this returns a clear error (use hypertune_query with a known flag name instead).
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  • Self-introspection: resolve the caller's identity + entitlement + granted scopes. Returns {subject, type, active, plan, scopes, export_cap}. Works when the account has no active plan (active:false), so an agent can check access before it calls a data tool.
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  • Searches concepts by their words and by meaning, across every tree or some. By words: every word must match a word in the concept's title, description, tags, ID, or body, or the start of one (`pol` finds `policy`; `fund` doesn't find `refund`); case doesn't matter. By meaning: concepts that mean what the query asks, in other words or in another language, so describe what you need in a phrase or a question ("how we recognize revenue") rather than a single word, which matches by meaning poorly. Filter by types, tags, statuses, or trust tiers: a concept passes a filter with any of its values, and must pass every filter given. An empty query lists every concept that passes the filters.
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  • Return the full ordered prerequisite chain for a concept — everything to learn first. Args: concept: Target concept — e.g. 'Speculative Decoding', 'Isaac Lab', 'NeMo Guardrails'. domain: Domain name from list_domains().
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  • Lists Hemrock financial-modeling concept docs — the methodology behind the math (exit waterfalls, anti-dilution, convertibles, cohorts, circular references, unit economics, and more). Returns a compact index of slugs, titles, and descriptions. Optionally filter by template_name or tag. Call get_concept to read the full text of any entry.
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