"Understanding the concept of dependency" matching MCP tools:

• find_equivalent

  Medical Terminologies MCP

  Ranked unified search for equivalent terms across multiple medical terminologies. Use this tool to: - Find the same concept in different coding systems - Compare how terminologies represent a concept - Support terminology mapping and data integration Searches across: ICD-11, SNOMED CT, LOINC, RxNorm, and MeSH. Set `target_terminologies` to limit which are searched, or set `source_terminology` to exclude one (e.g. when you already have a code from that terminology and want equivalents elsewhere). The two combine: source is subtracted from targets. `limit` caps candidates per terminology (default 5, max 10). Every candidate carries `match_score` (lexical similarity to the search term, 0-1) and `rank` (global position across all searched terminologies) — both computed by this server, since upstreams don't expose comparable relevance scores. Candidates from different terminologies whose titles are lexically identical are clustered in `groups` — a strong same-concept signal (absence of a group is NOT evidence of non-equivalence). Searches upstreams in English. For official pt-BR content, use the dedicated tools: `icd11_search`/`mesh_search` accept `language: "pt"`, and `cid10_search` is natively Portuguese.

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• search

  Islam West Africa Collection (IWAC)

  Search the Islam West Africa Collection across newspaper articles, Islamic publications, archival documents, academic references, audiovisual recordings, photographs, and the authority index (persons/places/organisations/events/subjects). Pass ONE concept or name — e.g. 'Tijaniyya', 'laïcité', 'Sheikh Gumi', 'pèlerinage'. Matching is accent- and case-insensitive; a multi-word query requires every word to appear somewhere in the item, so prefer a single concept per call. Write query strings and concept keywords in French for press/publication/document/index discovery even when the user's report language is not French. Academic references are multilingual, so try French and English title/abstract terms when relevant; metadata/filter labels remain French. Use the French transliteration of Islamic terms (Tabaski not 'Eid al-Adha', charia not 'sharia', Maouloud not 'Mawlid'). Returns {results:[{id,title,url,category}], ranking}; each result's `category` names its subset and the `ranking` field documents the ordering. Pass an id to `fetch` to read the full text. For filtered queries (by country, date, or newspaper) use the search_* tools instead.

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• beoordeel_tekst

  NeoDemos

  Eén deterministische review-tool voor concept-teksten van een raadslid/fractiemedewerker. Geen LLM, geen DB, <10ms. Kies de modus via `soort`: - `vragen` — scherpt concept schriftelijke/mondelinge vragen aan: flagt suggestief/sturend, meervoudig, gesloten ja/nee, vage kwantoren, en ongefundeerde vragen + 'scherper'-suggestie. - `notitie` / `commissienotitie` / `fractienotitie` — sanity-review (maximaal 10 opmerkingen): ontbrekend voor/tegen-eindoordeel, strategische opstelling, bronnen zonder paginanummer, ontbrekende samenvatting/vragen/bolletjes en suggestieve vragen — gegroepeerd op ernst (hoog/midden/laag). - `spreektekst` — rubriek met cijfer (1-10) + deelscores + verbeterpunten + duur-vs-spreektijd (opening, standpunt, onderbouwing, weerlegging, oproep, lengte ~130 wpm). - RvO-format check: `motie` | `motie_vreemd` | `amendement` | `schriftelijke_vragen` | `mondelinge_vragen` | `initiatiefvoorstel` | `interpellatieverzoek` — valideert structuur + RvO-regels (ontbrekend dictum, geen raadsvoorstel-ref bij amendement, gesloten vragen bij schriftelijke vragen, etc.). Gebruik dit na `genereer_raadsstuk` of op een handgeschreven concept. Gebruik wanneer: het raadslid een concept heeft geschreven en wil weten wat scherper kan. NIET om corpus te doorzoeken → `zoek_raadshistorie`. Retourneert: markdown met concrete verbeterpunten passend bij `soort`, gegroepeerd op ernst. **Positie in de drafting-keten:** roep aan ná `genereer_raadsstuk`; verwerk de bevindingen in het concept en sla daarna op met `sla_fractie_artifact_op(artifact_type=<doc_type>)`.

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• genereer_raadsstuk

  NeoDemos

  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>)`.

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• secedgar_get_snapshot

  secedgar-mcp-server

  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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• gbif_get_species_classification

  gbif-biodiversity-mcp-server

  Return the parent chain for a taxon — from kingdom (or domain) down to the immediate parent of the queried taxon — as an ordered array. Each entry has its rank, canonical name, and taxon key. The array is returned root-first (kingdom → phylum → class → … → immediate parent of the queried taxon); the queried taxon itself is not included — call gbif_get_species for its own record. Useful for building taxonomic trees or understanding placement without navigating the backbone level-by-level.

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• describe_services

  Venture Insights Concept Diagnostic

  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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• find_similar_creatives

  CreativeScope — Mobile Game Ad Creative Intelligence

  Find visually similar creatives using the stored vector of an existing creative. For a concept without an ID, query selects an explainable seed from available creative metadata and then uses the same vector-neighbor search. For an English concept, send the original English terms only. The service resolves Chinese source-label equivalents internally before selecting the seed. Returns creative records ordered from most to least visually similar; low-similarity and near-duplicate results are excluded, and raw similarity scores are not exposed. If request_echo.seed_basis identifies a proxy seed, clearly disclose that limitation instead of presenting the results as an exact concept match. Example: 'Show variants of the toilet run viral creative concept.'

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• get_top_observations

  Australian Economic Data (ABS, RBA & APRA)

  Source-aware convenience wrapper for highest or lowest numeric observations. For a single curated indicator prefer get_economic_series(concept=...), which resolves one series; pass series_ids=[...] to narrow a broad dataset instead of returning every series it contains.

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• tf_service_status

  terminalfeed

  Fetches operational status of major dev infrastructure (GitHub, Cloudflare, Discord, OpenAI, Vercel, npm, Reddit, Atlassian, Anthropic). Cache TTL 60s. Use when the agent needs to know if a dependency is up or to explain a recent outage.

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• get_categories

  Radix Wiki

  Get the wiki tag hierarchy with page counts per category. Useful for understanding what content exists, and for finding a valid tagPath before writing.

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• web_snapshot

  Robot Actions — Remote Device Control

  Accessibility tree of the DESKTOP grid browser page (by pageId), as text — for finding elements and understanding layout. Not a device: the equivalent for a phone or tablet is webpage_snapshot (by udid).

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• library_get_family

  luxalgo-mcp-server

  A family's hub page as markdown — the written overview of that school of analysis plus its complete concept roster. Use after library_list_families, or when the user asks about a whole area like 'SMC' or 'Wyckoff'.

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• get_grade_progression

  BC Curriculum

  Show how Big Ideas, Competencies, and Content progress across grade levels for a BC subject. Useful for understanding scaffolding, prerequisites, and learning trajectories. When a query is provided, filters to only matching items at each grade — showing a focused vertical thread rather than a full data dump. Args: - subject (string): Subject slug - grade_from (integer): Starting grade (0=K, 1-12) - grade_to (integer): Ending grade (0=K, 1-12) - focus (string, optional): Which element to trace ('big_ideas', 'competencies', 'content', 'all'). Default 'all'. - query (string, optional): Focus on a specific concept (e.g., 'evidence', 'multiplication'). Only matching items shown at each grade. Returns: Grade-by-grade breakdown of curriculum elements showing progression, optionally filtered to a concept thread.

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• open_diagnostic_intake

  Venture Insights Concept Diagnostic

  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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• route_query

  NVIDIA AI CKG

  Route an NVIDIA AI question to the optimal model and reasoning approach via graph depth. The CKG graph IS the router — hop depth is a deterministic complexity metric. Deeper NVIDIA prerequisite chains (CUDA → TensorRT → TensorRT-LLM → NIM) require more capable models. No heuristic: the graph decides. Routing table: hop_depth 1 → haiku · direct (simple lookup) hop_depth 2 → sonnet · generic_cot (moderate chain) hop_depth 3+ → opus · sparql_cot (deep dependency, structured reasoning) Args: question: Concept name or natural language question about NVIDIA AI. domain: Domain from list_domains() — e.g. "nvidia-tensorrt-triton", "nvidia-nim". Returns: model_tier + reasoning_approach + why + context subgraph to inject before LLM call.

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• entity_lookup

  The Chancery Lane Project

  Find TCLP content nodes (clauses, glossary terms) associated with a named concept. Unlike `search`, this performs a deterministic name match against Entity nodes in the knowledge graph rather than a relevance-ranked semantic search. Use it when you have a specific term or concept (e.g. "scope 3 emissions", "net zero") and want to retrieve every clause or glossary entry that explicitly references it. Args: name: The entity or concept name to look up (exact match, case-insensitive). limit: Maximum number of results to return (1–50). include_full_text: Include each hit's full body text (Markdown). Off by default — bodies are large; request only when you need the content, and prefer a small `limit` when you do. Returns: JSON with "meta" and "results" where each hit includes the source content node and the entity names that matched.

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• get_trident_spec

  trident

  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.

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• openlibrary_get_work

  openlibrary-mcp-server

  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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• list_categories_api_agents_categories_get

  Agentry — The Trust Layer for the Agent Economy

  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 } ] ```

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