"Understanding the Concept of LLM Context" matching MCP tools:

• query_intersectA

  NVIDIA NemoClaw CKGA

  Answer a conjunctive query: concepts reachable from EVERY anchor at once (A AND B). query_ckg walks outward from one concept. This intersects the reachable sets of two or more, which is the shape of most real questions — "the component that satisfies A AND applies to B". Neither anchor alone answers it; the answer lives in the overlap. Every branch is an exact set of declared edges, so the intersection is exact. A concept appears only if a declared path reaches it from each anchor. A relation missing from the graph produces an empty result, never a guess. Args: branches: Two or more branches. Either a bare anchor ("TensorRT-LLM"), which takes everything within `depth` hops, or an anchor plus an explicit relation path using '>' ("TensorRT-LLM > REQUIRES > ENABLES"), where each relation replaces the frontier. '*' matches any relation. Mix both forms freely. depth: Hops for bare-anchor branches, 1-5 (default 2). Ignored for explicit paths. direction: 'out' follows dependencies, 'in' follows them backwards, 'both' (default). mode: 'AND' (default) intersects branches; 'OR' unions them. limit: Max concepts listed, 1-200 (default 40). The true count is always shown. Returns: Markdown with the query plan and its per-step set sizes, then the answer set with taxonomy tags. Reports which branch was empty when the intersection is empty.

  ConnectorNo auth

• query_intersectA

  Salesforce AgentForce CKGA

  Answer a conjunctive query: concepts reachable from EVERY anchor at once (A AND B). query_ckg walks outward from one concept. This intersects the reachable sets of two or more, which is the shape of most real questions — "the component that satisfies A AND applies to B". Neither anchor alone answers it; the answer lives in the overlap. Every branch is an exact set of declared edges, so the intersection is exact. A concept appears only if a declared path reaches it from each anchor. A relation missing from the graph produces an empty result, never a guess. Args: branches: Two or more branches. Either a bare anchor ("TensorRT-LLM"), which takes everything within `depth` hops, or an anchor plus an explicit relation path using '>' ("TensorRT-LLM > REQUIRES > ENABLES"), where each relation replaces the frontier. '*' matches any relation. Mix both forms freely. depth: Hops for bare-anchor branches, 1-5 (default 2). Ignored for explicit paths. direction: 'out' follows dependencies, 'in' follows them backwards, 'both' (default). mode: 'AND' (default) intersects branches; 'OR' unions them. limit: Max concepts listed, 1-200 (default 40). The true count is always shown. Returns: Markdown with the query plan and its per-step set sizes, then the answer set with taxonomy tags. Reports which branch was empty when the intersection is empty.

  ConnectorNo auth

• 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

• search_d365_codeA

  ALM X++ MCP ServerA

  WHEN: object name is unknown, partial, or you need to find by concept/keyword. Search the D365 F&O knowledge base for X++ code, tables, classes, forms, views, enums, EDTs, security objects using natural language or partial names. Returns ALL chunks (metadata, Declaration, methods) for the top-scoring objects so the LLM has complete context on the first call. Lower-scoring results return a short preview. No follow-up get_object_details call is needed for top results. NOT for listing all objects in a model -- use list_objects for that. NOT when the exact name is known -- use get_object_details for that. NEVER call search_d365_code twice in the same conversation turn. If one search did not find the object, answer from what you have -- do not repeat the search. When you need context on MORE THAN ONE concept simultaneously, use batch_search instead -- it runs all queries in parallel and is faster. NEVER call for ADO items (FDD, RDD, IDD, Bug, Task, PR, WorkItem, sprint, #1234) -- use ado_* tools instead.

  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

• merge_semantic_conceptsA

  Entity EnricherA

  Merge a loser concept into a winner. Defaults to impact_only=true: inspect counts and affected replicas before obtaining approval to execute. impact_only=false requires owner and rewrites aliases, entity identities and referencing payloads, queuing convergence to linked replicas. No LLM call. Similarity alone does not establish identity; inspect get_semantic_concept and the judge review evidence first. Returns impact or merge results. 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

• talonic_find_dataA

  Talonic - Data Extraction MCP

  Locate the REAL data behind a natural-language concept before querying anything: semantic + lexical retrieval that resolves a phrase ('payment volume per transaction', 'counterparty', 'Vertragslaufzeit') to the registry fields, values, documents and text passages that carry it — even when the field is captured under a different name. USE WHEN: the user asks about a concept and you are not sure which field holds it, when talonic_list_fields / talonic_search came back empty or ambiguous, or when the answer may live in document prose rather than a captured cell. NOT FOR: reading a known field's values (talonic_field_values) or filtering by a known field (talonic_filter). ARGS: `query` (the concept, in the user's words), optional `top_k` (1–25, default 10), `document_ids` (hard scope). RETURNS: ranked planes — FIELDS (canonical_name, field ids/keys, maturity/tier, occurrence_count, sample values with their documents), VALUES, DOCUMENTS and PASSAGES — every item a ready handle for the next call. Read-only, no LLM cost.

  ConnectorOAuth

• post_debug_codeA

  webbersites-x402-data-apiA

  LLM CODE DEBUGGING — POST {code, error} and get a diagnosis: what is wrong, the root cause, and a concrete fix with corrected code. Paste the failing snippet plus the error message or stack trace; any language, up to 20,000 chars combined. Optional {language} and {context} ('happens only on the second call'). Fast cheap LLM under the hood. Want deterministic no-AI lint instead? POST /api/lint/:language ($0.002). ($0.01 per call, paid via x402)

  ConnectorNo auth

• omnarai_contextA

  omnarai-mcpA

  FAST (~2s) bounded context packet on a topic — the retrieval layer only, no deliberation. Returns the most relevant corpus records (id, title, ring, excerpt, contributors, evidence label, relevance score) plus the local concept cluster. Your default orientation on any Omnarai topic. Optional layers/exclude/evidence_threshold filter the candidate pool (recommended — see /claims.json).

  ConnectorNo auth

• beoordeel_tekstA

  NeoDemosA

  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>)`.

  ConnectorNo auth

• create_studyA

  User IntuitionA

  Create an in-depth-interview, concept-test, or prototype-test draft using ordinary metadata only. Ask the human to explicitly choose recruiting_method (panel, BYOP, 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 Elliot; 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. The Customize Plan backend validates and attaches the asset. Voice configuration is kept for chat as well as voice and video.

  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

• market_analyzeA

  x402 Crypto Market StructureA

  Is macro with you or against you? Get the current regime (bull/bear/risk_on/risk_off/choppy), directional signal and confidence, and macro context (DXY, VIX, fear/greed) before entering a position. Data-only, no LLM latency. coverage disclosed per token. REST equivalent: POST /analyze/market (0.25 USDC). Args: token: Token symbol (BTC, ETH, SOL, XRP, ADA, DOGE, AVAX, LINK, BNB, ATOM, DOT, ARB, SUI, OP, LTC, AMP, ZEC) context: Optional historical context window ('7d' or '30d'). Adds percentile rankings.

  ConnectorNo auth

• 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

• explain_conceptA

  MERX - TRON Resource ExchangeB

  Explain a TRON or Merx concept in plain language. Hardcoded topics (exact match): energy, bandwidth, staking, delegation, sun_units, burn_vs_rent, merx_routing, provider_types. The lookup is fuzzy — substring matches also work, so "rent" finds "burn_vs_rent" and "providers" finds "provider_types". For topics outside this list (e.g. x402, stablecoins, gasfree), pick the closest hardcoded topic, or just answer the question yourself from the broader context — this tool only returns canned explanations of TRON resource economics.

  ConnectorNo auth

• get_semantic_conceptA

  Entity EnricherA

  Read one concept's aliases, identity source keys, linked records and nearest neighbors within its own type/model slice. Record links are capped; records_truncated signals omitted links. neighbors_limit=0 skips neighbors. Use returned alias IDs with update_concept_alias and similarities to assess a proposed merge. No LLM call. Never compare similarities across embedding spaces or concept types. See enricher://docs/semantic-ids.

  ConnectorOAuth

• 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

• 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