"Understanding the concept of perplexity" matching MCP tools:

• describe_servicesA

  Venture Insights Concept DiagnosticA

  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.

  ConnectorNo auth

• genereer_raadsstukA

  NeoDemosA

  Genereert een volledig opgemaakt, RvO-conformant concept voor één van de zeven raadsstuk­typen. Geen DB, geen netwerk, <10ms — puur structurele kennis. Gebruik deze tool wanneer: - Het raadslid een stuk wil indienen en een correct gestructureerd concept nodig heeft. - Je `adviseer_raadsinstrument` hebt gebruikt (welk instrument) en nu het daadwerkelijke stuk wilt renderen in het juiste format met RvO-verwijzingen. - Een concept al bestaat maar opnieuw in correct format moet worden gezet. Gebruik deze tool NIET wanneer: - Je het juiste instrument nog moet kiezen → gebruik eerst `adviseer_raadsinstrument`. - Je een bestaand concept wilt beoordelen op inhoud → `beoordeel_tekst`. - Je een format-validatie wil uitvoeren op een bestaand concept → `beoordeel_tekst` met `soort` gelijk aan het doc_type. ``doc_type`` keuzes: 'motie', 'motie_vreemd', 'amendement', 'schriftelijke_vragen', 'mondelinge_vragen', 'initiatiefvoorstel', 'interpellatieverzoek'. ``velden`` zijn optioneel — ontbrekende velden worden vervangen door invul-placeholders [zoals dit] zodat het concept altijd een compleet, geldig skelet is. ``gemeente`` bepaalt welk lokaal RvO-overlay (artikel­nummers, termijnen, indienings­route) wordt gebruikt. Default 'rotterdam'. Degradeert netjes naar het canonieke basis­format + disclaimer als er geen overlay beschikbaar is. Retourneert: markdown-concept in de juiste RvO-structuur, met RvO-artikel­citaat en disclaimer. **Volgende stap in de drafting-keten:** valideer het gegenereerde concept met `beoordeel_tekst(tekst=<concept>, soort=<doc_type>)` voordat je het presenteert of opslaat; sla daarna op met `sla_fractie_artifact_op(artifact_type=<doc_type>)`.

  ConnectorNo auth

• add_semantic_conceptA

  Entity EnricherA

  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.

  ConnectorOAuth

• update_concept_aliasA

  Entity EnricherA

  Remove or promote a concept alias using alias IDs from get_semantic_concept. Requires editor; no LLM call. action='remove' stops that surface form resolving to this concept; removing the last alias is refused. action='set_canonical' changes its displayed form. To add an alias use add_semantic_concept(alias_of=...). Returns the outcome. See enricher://docs/semantic-ids.

  Connector

  Destructive

  OAuth

• search_recipesA

  weReciA

  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.

  ConnectorOAuth

• connectA

  Penfield MemoryA

  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

  ConnectorOAuth

• secedgar_get_snapshotA

  secedgar-mcp-serverA

  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.

  ConnectorNo auth

• enrich_providerA

  apis-ioB

  One call, pick your field groups — resolves a slug OR any identifier and returns exactly the groups you ask for, instead of chaining get_provider + get_provider_rating + get_provider_artifacts + get_provider_onboarding. Groups: profile, onboarding, artifacts, rating, insights. Understanding plan — the base groups moved with the rest of the discovery layer on 2026-08-31. Priced B2 (cross-catalog synthesis) — $0.05 per call under pay-as-you-go; included in Understanding and Influence. See apis://prices.

  ConnectorNo auth

• eurlex_browse_subjectsA

  eur-lex-mcp-serverA

  Search the EuroVoc thesaurus, resolving a keyword into concept URIs usable in the eurovoc_concept subject filter of eurlex_search_documents. 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.

  ConnectorNo auth

• open_diagnostic_intakeA

  Venture Insights Concept DiagnosticA

  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.

  ConnectorNo auth

• get_xbrlA

  Edgrapi — US Government Data APIA

  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.

  ConnectorNo auth

• get_xbrlA

  edgrapi-skillsA

  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.

  ConnectorNo auth

• insights_industriesA

  apis-ioB

  UNDERSTANDING — the demand-side industry rollup: the sectors the profiled companies fall into. Each row carries TWO counts and they answer different questions: `company_count` is the research roster, `profiled_count` is how many of those you can actually read back via find_company_insights(industry:) — size a cohort on profiled_count. The counterpart to find_industries, which counts SUPPLY (providers publishing APIs into a vertical) — this counts DEMAND (companies buying into it). Comparing the two is how you find a sector with buyers and no sellers. Priced B2 (cross-catalog synthesis) — $0.05 per call under pay-as-you-go; included in Understanding and Influence. See apis://prices.

  ConnectorNo auth

• fetchA

  EventTrader MCPB

  Fetch the full text of a search result by id: answer:return-paths (NEXUS's grounded how-to-make-a-return answer), fund:<SYMBOL> (one fund in full public detail), concept:<slug> (an encyclopedia entry). Read-only, no key.

  ConnectorNo auth

• probe_semantic_conceptA

  Entity EnricherA

  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.

  ConnectorOAuth

• fetchA

  EventTrader Research (read-only)B

  Fetch the full text of a search result by id: answer:return-paths (NEXUS's grounded how-to-make-a-return answer), fund:<SYMBOL> (one fund in full public detail), concept:<slug> (an encyclopedia entry). Read-only, no key.

  ConnectorNo auth

• get_trident_specA

  tridentA

  Returns the complete Trident 2D specification including grammar, syntax rules, coordinate system, containers, nodes, connections, shapes, and icon reference. Use this when you need deep understanding of the Trident DSL.

  ConnectorNo auth

• list_categories_api_agents_categories_getA

  Agentry — The Trust Layer for the Agent EconomyB

  List Categories List all agent categories with counts. Returns every category in the directory along with the number of agents in each. Useful for building category filters or understanding the directory's coverage areas. ### Responses: **200**: Successful Response (Success Response) Content-Type: application/json **Example Response:** ```json [ { "category": "Category", "count": 1 } ] ```

  ConnectorNo auth

• get_mission_statsA

  mcp-serverA

  Get aggregate statistics about missions on the HomeVisto platform. Returns total counts, status breakdown, and average bounty information. Useful for understanding platform activity.

  ConnectorNo auth

• get_knowledge_statsA

  mcp-klever-vmA

  Get summary statistics of the Klever VM knowledge base. Returns total entry count, counts broken down by context type (code_example, best_practice, security_tip, etc.), and a sample entry title for each type. Useful for understanding what knowledge is available before querying.

  ConnectorNo auth

• vector_quantizeA

  IA-QA — 130+ QA & Dev Tools for AI AgentsB

  Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precision loss (MSE). Useful for understanding vector DB compression trade-offs.

  ConnectorNo auth