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203,652 tools. Last updated 2026-06-14 20:25

"Understanding the concept of perplexity" matching MCP tools:

  • Hybrid search — combines keyword + semantic search via RRF. Uses Reciprocal Rank Fusion (RRF) to merge exact-word results with meaning-based results. **This is the recommended tool for "discourses about X" / concept queries**, because the semantic side catches suttas that discuss a concept using different vocabulary (e.g. some mindfulness-of-breathing suttas use `assasati/passasati/dīghaṁ` instead of `ānāpānassati`). 💡 **Hints for the AI client:** - English queries usually work best (e.g. `mindfulness of breathing`) because the embedding model is multilingual but EN-primary. - Thai stop-word handling is weak. If a Thai query underperforms, the AI client should translate to Pāli/English first (see server instructions). - The default `limit=5` is often too small for a topic survey — use `limit=15-20` (max 20) for good coverage. - Ranking is by similarity, NOT canonical importance — locus classicus suttas (e.g. MN118, DN22) may rank below smaller suttas that happen to use the exact vocabulary. Treat results as a starting point, then call `get_sutta` for the canonical references.
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  • Use this tool when the user asks BOTH what a financial figure is AND which filing reported it — for example "What was Apple's most recently reported revenue, and which 10-Q filed it?" or "Show me the accession ID for Tesla's latest net income" or "Which filing form reported Amazon's Q3 operating cash flow?" This tool returns a single fact plus its complete filing provenance: entity, concept, period, value, accession ID, filing URL, and form type (10-K, 10-Q, etc.). Use this INSTEAD OF `search_companies` when the user already names a company and wants a financial figure with its source filing — `search_companies` only resolves company identifiers and returns no financial data. Use this INSTEAD OF `get_company_fundamentals` when the user explicitly wants to know which filing or form type reported a number, or needs the accession ID — `get_company_fundamentals` returns metrics across multiple periods but omits filing provenance. Two lookup modes: (1) by fact_id (SHA-256 hash of entity_id|accession_id|concept|period_end|unit) for deterministic identity; or (2) by concept name (e.g., TotalRevenue, NetIncome, EPSDiluted, TotalAssets, OperatingCashFlow) plus a ticker to retrieve the most recently reported fact. Optionally pin a point-in-time cutoff via as_of_date (YYYY-MM-DD) — returns the latest filing accepted by SEC on or before that date, eliminating look-ahead bias. Check `_meta.pit_safe` in the response to confirm PIT correctness. DURATION: income-statement flow concepts (NetIncome, TotalRevenue, etc.) are reported over a window, and a single 10-K tags BOTH a 12-month figure and a 3-month Q4 stub at the same fiscal-year-end period_end. On a tie this tool returns the longer (headline) window, and every result carries `period_type` (instant | quarterly | half_year | nine_month | annual | duration) and `period_span_days` so you always know whether a number is a quarter or a full year — never present a 3-month stub as the annual figure. Provide either fact_id or concept (required). Returns empty result with error_code FACT_NOT_FOUND if no matching fact exists for the given concept and ticker. Available on all plans.
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  • Return a canonical definition for a primitive Eurorack / synthesis concept and its relations to other concepts in the corpus. Use this for VOCABULARY questions, not module questions — when the user is asking what a term means or how two terms relate, not which modules implement it. Typical shapes: - "Is four-quadrant mult the same as through-zero AM?" → lookup_concept("four-quadrant mult") - "What's the difference between a gate and a trigger?" → lookup_concept("gate") - "Modular signal level vs line level — when does it matter?" → lookup_concept("modular signal level") - "Are clock dividers just pulse counters?" → lookup_concept("clock divider") - "Are polyphonic patch cables TRRRRRS?" → lookup_concept("polyphonic cable") Lookup is case-insensitive across three axes, tried in order: the canonical id ("through-zero-fm"), the canonical label ("Through-Zero FM (TZFM)"), and any registered alias ("tzfm", "through zero fm"). Spaces and hyphens are matched literally; the lookup does NOT normalize whitespace beyond lowercasing. If the term doesn't match anything, the response includes up to 5 substring-matched suggestions. Args: - name (string, required, min length 2): the term to look up. Examples: "AM", "ring mod", "four-quadrant mult", "TZFM", "clock divider", "gate", "trigger". Returns: { "concept": { "id": "amplitude-modulation", "label": "Amplitude Modulation (AM)", "description": "A multiplication of two signals: the carrier...", "aliases": ["am", "amplitude modulation", "amplitude mod"], "related_concepts": [ { "related_concept_id": "ring-modulation", "related_concept_label": "Ring Modulation (RM)", "relation_kind": "commonly_confused_with", "note": "AM with a unipolar modulator preserves the carrier..." }, ... ], "source_id": null, "citation_url": "https://learningmodular.com/glossary/...", "citation_quote": "Amplitude modulation is when..." } | null, "_meta": { "query": "<the name argument verbatim>", "matched_via": "id" | "label" | "alias" | "none", "concept_suggestions": [ { "id": "...", "label": "...", "matched_via": "alias", "matched_text": "..." } ], "feedback_hint": "...?" } } Relation kinds: - "related_to" — see-also link (default; symmetric in spirit). - "subtype_of" — X is a specific case of Y (RM ⊂ AM, TZFM ⊂ linear FM). - "inverse_of" — X is the opposite of Y (clock-divider ↔ clock-multiplier). - "commonly_confused_with" — they're distinct, but people conflate them (gate vs trigger, AM vs RM, modular level vs line level). When to cite: every concept carries either source_id or citation_url + citation_quote. Surface the citation when the answer affects a decision (e.g. "the corpus cites learningmodular.com — TRS cables are physically the same connector whether carrying balanced mono or unbalanced stereo; only the destination determines the role"). When the result is null and concept_suggestions are provided, present 2–3 closest matches to the user. If none look right, the corpus genuinely doesn't carry that concept — call report_gap with kind="missing_field" and tool_name="lookup_concept" naming the term and its expected definition.
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  • Get comprehensive RDF data for a DanNet synset (lexical concept). UNDERSTANDING THE DATA MODEL: Synsets are ontolex:LexicalConcept instances representing word meanings. They connect to words via ontolex:isEvokedBy and have rich semantic relations. KEY RELATIONSHIPS (by importance): 1. TAXONOMIC (most fundamental): - wn:hypernym → broader concept (e.g., "hund" → "pattedyr") - wn:hyponym → narrower concepts (e.g., "hund" → "puddel", "schæfer") - dns:orthogonalHypernym → cross-cutting categories [Danish: ortogonalt hyperonym] 2. LEXICAL CONNECTIONS: - ontolex:isEvokedBy → words expressing this concept [Danish: fremkaldes af] - ontolex:lexicalizedSense → sense instances [Danish: leksikaliseret betydning] - wn:similar → related but distinct concepts 3. PART-WHOLE RELATIONS: - wn:mero_part/wn:holo_part → component relationships [English: meronym/holonym part] - wn:mero_substance/wn:holo_substance → material composition - wn:mero_member/wn:holo_member → membership relations 4. SEMANTIC PROPERTIES: - dns:ontologicalType → semantic classification with @set array of dnc: types Common types: dnc:Animal, dnc:Human, dnc:Object, dnc:Physical, dnc:Dynamic (events/actions), dnc:Static (states) - dns:sentiment → emotional polarity with marl:hasPolarity and marl:polarityValue - wn:lexfile → semantic domain (e.g., "noun.food", "verb.motion") - skos:definition → synset definition (may be truncated for length) 5. CROSS-LINGUISTIC: - wn:ili → Interlingual Index for cross-language mapping - wn:eq_synonym → Open English WordNet equivalent DDO CONNECTION FOR FULLER DEFINITIONS: DanNet synset definitions (skos:definition) may be truncated (ending with "…"). For complete definitions, use the fetch_ddo_definition() tool which automatically retrieves full DDO text, or manually examine sense source URLs via get_sense_info(). NAVIGATION TIPS: - Follow wn:hypernym chains to find semantic categories - Check dns:inherited for properties from parent synsets - Use parse_resource_id() on URI references to get clean IDs - For fuller definitions, examine individual sense source URLs via get_sense_info() Args: synset_id: Synset identifier (e.g., "synset-1876" or just "1876") Returns: Dict containing JSON-LD format with: - @context → namespace mappings - @id → entity identifier (e.g., "dn:synset-1876") - @type → "ontolex:LexicalConcept" - All RDF properties with namespace prefixes (e.g., wn:hypernym) - dns:ontologicalType → {"@set": ["dnc:Animal", ...]} (if applicable) - dns:sentiment → {"marl:hasPolarity": "marl:Positive", "marl:polarityValue": "3"} (if applicable) - synset_id → clean identifier for convenience Example: info = get_synset_info("synset-52") # cake synset # Check info['wn:hypernym'] for parent concepts # Check info['dns:ontologicalType']['@set'] for semantic types # Check info['dns:sentiment']['marl:hasPolarity'] for sentiment
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  • Like palette_concept but with archive filtering and relevance controls. Use allowed_archives to restrict results to specific cultural traditions e.g. ['Japan'] for Japanese only. Use min_relevance to filter weak concept matches. Fixes cross-archive drift when cultural specificity matters.
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  • Search the BLS series catalog by natural language query, survey code, geographic area, or keywords to resolve cryptic SeriesIDs. Returns matching series with decoded components (survey, area, item, seasonal flag) and plain-language names. Use this before bls_get_series when you have a concept but not a SeriesID. Operates offline — no API quota consumed. Survey filter accepts two-letter codes (CU, CE, LN, LA, PC, JT, OE, EC, PR). Area filter accepts state names, MSA names, or FIPS area codes.
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  • Enable AI assistants to perform web searches using Perplexity's Sonar Pro.

  • Guardian Open Platform: content search, articles, sections, tags. Free dev key.

  • Search the 96-indicator registry by keyword. Returns ranked matches (up to `limit`, default 10, max 50) with slug, branded name, underlying name, category, and canonical URL. Scoring is substring+prefix over slug, branded_name, name, and category — e.g. query 'savings' returns both The Buffer (personal saving rate) and The Safety Net (emergency savings survey). Use this when you want to discover which slug corresponds to a concept before calling `get_indicator`.
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  • Search EU legislation, treaties, and preparatory acts across the CELLAR corpus of 2.7M+ works. Filters by document type, date range, EuroVoc subject concept, author institution, and in-force status. Keyword search matches against English expression titles and CELEX strings — full-text body search is not available via this API. For multi-word searches, supply a single dominant keyword; use other filters to narrow results. Returns CELEX numbers, work URIs, human-readable document type labels, and dates — use these with eurlex_get_document to fetch full content. To filter by EuroVoc subject, first call eurlex_browse_subjects to obtain the concept URI. Case law (CJEU/GC judgments) is better searched via eurlex_get_cases which has court-specific parameters.
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  • Get a complete overview of all senses for a Danish word in a single call. Replaces the common pattern of calling get_word_synsets → get_synset_info per result → get_word_synonyms, collapsing 5-15 HTTP round-trips into one SPARQL query. Only returns synsets where the word is a primary lexical member (i.e. the word itself has a direct sense in the synset), excluding multi-word expressions that merely contain the word as a component. Args: word: The Danish word to look up Returns: List of dicts, one per synset, each containing: - synset_id: Clean synset identifier (e.g. "synset-3047") - label: Human-readable synset label - definition: Synset definition (may be truncated with "…") - ontological_types: List of dnc: type URIs - synonyms: List of co-member lemmas (true synonyms only) - hypernym: Dict with synset_id and label of the immediate broader concept, or null - lexfile: WordNet lexicographer file name (e.g. "noun.animal"), or null if absent Example: overview = get_word_overview("hund") # Returns list of 4 synsets, the first being: # {"synset_id": "synset-3047", # "label": "{hund_1§1; køter_§1; vovhund_§1; vovse_§1}", # "definition": "pattedyr som har god lugtesans ...", # "ontological_types": ["dnc:Animal", "dnc:Object"], # "synonyms": ["køter", "vovhund", "vovse"], # "lexfile": "noun.animal"} # Pass synset_id to get_synset_info() for full JSON-LD data on any result: # full_data = get_synset_info(overview[0]["synset_id"])
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  • Generate a complete interior colour specification from a concept or brief. Input a room concept, type, and style — receive a professionally structured colour scheme with 60/30/10 surface assignments, archive colour names with full cultural provenance, Farrow and Ball and Little Greene paint matches, three-illuminant light behaviour (D65 daylight, F11 atrium, Illuminant A incandescent), WCAG accessibility for digital use, and a written cultural rationale explaining why each colour belongs in this room. Examples: 'bold maximalist living room', 'calm Scandi bedroom', 'Victorian study', 'coastal kitchen', 'gallery hallway'. Use /interior-specification/pdf for a downloadable branded PDF version. This is the tool that replaces a colour consultation.
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  • Connect memories to build knowledge graphs. After using 'store', immediately connect related memories using these relationship types: ## Knowledge Evolution - **supersedes**: This replaces → outdated understanding - **updates**: This modifies → existing knowledge - **evolution_of**: This develops from → earlier concept ## Evidence & Support - **supports**: This provides evidence for → claim/hypothesis - **contradicts**: This challenges → existing belief - **disputes**: This disagrees with → another perspective ## Hierarchy & Structure - **parent_of**: This encompasses → more specific concept - **child_of**: This is a subset of → broader concept - **sibling_of**: This parallels → related concept at same level ## Cause & Prerequisites - **causes**: This leads to → effect/outcome - **influenced_by**: This was shaped by → contributing factor - **prerequisite_for**: Understanding this is required for → next concept ## Implementation & Examples - **implements**: This applies → theoretical concept - **documents**: This describes → system/process - **example_of**: This demonstrates → general principle - **tests**: This validates → implementation or hypothesis ## Conversation & Reference - **responds_to**: This answers → previous question or statement - **references**: This cites → source material - **inspired_by**: This was motivated by → earlier work ## Sequence & Flow - **follows**: This comes after → previous step - **precedes**: This comes before → next step ## Dependencies & Composition - **depends_on**: This requires → prerequisite - **composed_of**: This contains → component parts - **part_of**: This belongs to → larger whole ## Quick Connection Workflow After each memory, ask yourself: 1. What previous memory does this update or contradict? → `supersedes` or `contradicts` 2. What evidence does this provide? → `supports` or `disputes` 3. What caused this or what will it cause? → `influenced_by` or `causes` 4. What concrete example is this? → `example_of` or `implements` 5. What sequence is this part of? → `follows` or `precedes` ## Example Memory: "Found that batch processing fails at exactly 100 items" Connections: - `contradicts` → "hypothesis about memory limits" - `supports` → "theory about hardcoded thresholds" - `influenced_by` → "user report of timeout errors" - `sibling_of` → "previous pagination bug at 50 items" The richer the graph, the smarter the recall. No orphan memories! Args: from_memory: Source memory UUID to_memory: Target memory UUID relationship_type: Type from the categories above strength: Connection strength (0.0-1.0, default 0.5) ctx: MCP context (automatically provided) Returns: Dict with success status, relationship_id, and connected memory IDs
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  • Generate a complete colour direction package for another AI agent or image generation model. Fetches a historically grounded archive palette from the concept, then produces: an agent brief (colour direction in prose), colour tokens with hex values and roles, a model-specific image generation prompt, a negative prompt, and lighting notes. Supports midjourney, flux, dalle, stable_diffusion. Example: task='luxury hotel bedroom', concept='Ottoman winter luxury', model='midjourney'. Use this to make Colour Memory the colour layer for other AI systems.
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  • Persist a correction of a citation value. The correction is keyed on the canonical `fact_id` (a stable hash of CIK + accession + concept + period) so it applies to every report that references that same fact — including agent-regenerated reports. Re-saving the same fact_id replaces the prior correction in place (no duplicate row). The `fact_id` is VERIFIED against live SEC data (scoped to `ticker`) before the correction is stored — a fact_id that doesn't resolve to a real fact is rejected with FACT_NOT_FOUND and nothing is persisted. You therefore must supply the `ticker` the fact belongs to. Use this when the user notices an inaccuracy in an AI-generated report and wants the fix to persist. Provide `notes` for the rationale (≤500 chars) and `source_report_id` for provenance. Tier caps: sp500=500, pro=5000, full=50000.
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  • Get the live operational status of every major AI service tracked by TensorFeed (Claude, ChatGPT, Gemini, Perplexity, Cohere, Mistral, HuggingFace, Replicate, Midjourney, etc). Polled every 2 min. Returns operational | degraded | down per service plus the most recent incident.
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  • Like palette_concept but with archive filtering and relevance controls. Use allowed_archives to restrict results to specific cultural traditions e.g. ['Japan'] for Japanese only. Use min_relevance to filter weak concept matches. Fixes cross-archive drift when cultural specificity matters.
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  • Retrieve one exact SVG icon when the icon ID and library are already known. Use search_icons first if the user only described a concept. Returns SVG code and public semantic guidance for the exact icon.
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  • Fetch a work by Open Library Work ID (OL…W). Returns title, description, subjects, cover IDs, and linked author IDs for follow-up lookups. Works represent the abstract book concept independent of any specific edition. Note: author names are not included — use openlibrary_get_author or openlibrary_search_books for names.
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  • Return the complete parent chain for a taxon — from kingdom (or domain) down to the taxon itself — as an ordered array. Each entry has its rank, canonical name, and taxon key. The array is returned root-first (kingdom → phylum → class → … → parent of given taxon). Useful for building taxonomic trees or understanding placement without navigating the backbone level-by-level.
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  • Search the Jisho.org Japanese<->English dictionary. The keyword can be English (translate to Japanese), Japanese kanji/kana, or romaji. Returns up to `limit` matching dictionary entries, each with the headword (slug), whether it is a common word, JLPT level, all readings/spellings, and English meanings grouped into senses with parts of speech. Use this to translate, look up a kanji/kana word, or find Japanese words for an English concept.
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  • Get historical XBRL financial data for a company. Accepts friendly concept names (e.g., "revenue", "net_income", "assets") or raw XBRL tags. Discover available friendly names with secedgar_search_concepts. Handles historical tag changes and deduplicates data automatically.
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