"How to Query a Knowledge Graph Using an Ontology" matching MCP tools:

• methodology_faqA

  RefproA

  Answer questions about how Refpro works. Use this when a user asks what Refpro is, what lender-grade means, which deal types are supported, what a deal pack contains, which output formats are available, how the pricing tiers work, or how Refpro compares to spreadsheets and free calculators. Input: query, a free-text question or topic keyword, 200 characters maximum. Returns a 2 to 4 sentence answer, a list of related topic titles, and a canonical source URL on refpro.ai. Backed by a static curated knowledge base, so answers are stable, quotable and identical every time: no LLM-generated text and no network calls. Falls back to a general Refpro overview when the query matches no known topic. Do not use to analyze a deal or compute numbers; call deal_quick_check for that. Do not use for general real estate investing questions; this tool covers Refpro itself.

  ConnectorNo auth

• get_flow_contextA

  FlowCastleA

  Return one flow's graph topology: its blocks, how they link, and a short summary per block. Read-only. Deliberately omits block data and action configs to stay cheap — once you know which block matters, call get_block_details for its full contents. This is the normal first step before editing an existing flow.

  ConnectorNo auth

• graph_searchA

  Moltline RecallA

  Find what the graph knows about something, plus its neighbourhood. FREE. Scores entities by how many query words appear in the name, type and observations, then pulls in whatever is within the requested number of hops - because the useful answer to "what do we know about Acme" is usually Acme plus who it is connected to. Typical input {"graph": {...}, "query": "acme renewal", "hops": 1} returns {"matches": [{"name": "Acme Corp", "score": 3, "why": ["name", "observation"]}], "neighbourhood": {"entities": [...], "relations": [...]}, "hops": 1}. Use to read memory back before answering. Not for writing (graph_upsert) and not for narrowing by date, which graph_at_time does. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "query must contain at least one word or number"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• delete_knowledge_entryA

  AsyntaiA

  Permanently delete one knowledge base entry and every passage built from it. The chatbot stops using that content immediately. This cannot be undone.

  Connector

  Destructive

  OAuth

• query_knowledge_graphA

  Analytics Legends — SAP Analytics IntelligenceA

  The RELATIONS between the platform's teaching objects — which Academy module teaches which concept, which study covers which module, what a concept relates to. THIS IS THE ONLY TOOL ON THIS SERVER THAT SERVES EDGES; the others serve rows. Ask it what connects to what, not what exists. SCOPE, AND IT IS NARROWER THAN 'the knowledge graph': it carries four node types — `concept`, `module`, `study`, `vendor` — and every edge whose BOTH endpoints are one of them. The whole graph holds twelve node types; the eight it does not carry are each either served by their own tool or named as not served at all, and `_meta.excluded_node_types` says which per type (consultant data is served at NO tier), so a missing type is a documented boundary and never a silent gap. Call it with `node_id` (e.g. `module:M178`, `concept:C001`, `study:ai-impact-2026-EN`) to walk one node's neighbourhood; with `node_type` and/or `query` to find a node id first. `edge_type` and `direction` narrow a walk. Read `_meta.available_edge_types` — computed from the served projection on every call — before assuming an edge type exists.

  ConnectorNo auth

• graph_compactA

  Moltline RecallA

  Shrink a memory to the part that still earns its place. PREMIUM (license). Ranks entities by how connected they are and how much is recorded about them, keeps anything you name outright, and drops the rest along with the relations that pointed at them. Typical input {"graph": {...}, "max_entities": 50, "keep": ["Acme Corp"]} returns {"graph": {...}, "kept": 50, "dropped_entities": ["Old Note", ...], "dropped_relations": 12, "ranking": "degree, then observation count, then name"}. Use when a graph has outgrown the context you can spend on it. Not for removing wrong facts - graph_lint finds those, and deleting them is a decision you should make deliberately rather than by ranking. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• database_tools_execute_queryA

  Point Topic — UK Broadband Market ReferenceA

  Query Point Topic's public broadband ontology (ClickHouse) — read-only. Exposes the public reference tables (the visitor view): the entity graph of ISPs, network operators, networks, links, link standards and their relationships. Discover tables with SHOW TABLES FROM ontology; inspect columns with DESCRIBE TABLE <name>. Licensed measurement data (footprints, premises, speeds, tariffs, forecasts, take-up, subscribers) is not queryable here and requires a Point Topic licence — denied queries return contact details. Only SELECT/WITH/SHOW/DESCRIBE/EXPLAIN allowed; returns CSV (large results are truncated at ~50k tokens with a leading notice — add LIMIT to keep results small).

  ConnectorNo auth

• get_statsA

  TunnelMind Data APIB

  One public "state of the corpus" readout — the whole graph in a single call. Distinct from the Scry-only sensor stats at api.tunnelmind.ai/v1/stats (which this reuses for the `scry` block): this spans Scry, Sigil, and Tracker plus the attestation and routing layers. Use it to cite live coverage — how many publishers / SSPs / DSPs / sell paths / sellers.json seats are in the Sigil supply graph, how many tracker entities and domains Tracker holds, how many ATAP witness events and OAIs the attestation layer carries, and how many BGP watchlist resources and routing events the monitor has recorded. Every count is independent and null-tolerant: a momentarily-unavailable lens reports `null`, never a silent zero. Cacheable for ~10 minutes.

  ConnectorNo auth

• searchA

  Melvea Local Honey DiscoveryA

  Search the Melvea local honey directory by free-text query and return matching producers as a list of results (id, title, url). Designed for ChatGPT Deep Research and Company Knowledge. Use for any local-honey discovery query that names or implies a place; the tool parses place and varietal from the query. Returns an honest empty list when nothing matches — never fabricate. Pair with fetch to retrieve full producer detail.

  ConnectorNo auth

• search_kbA

  DeskCrewA

  Read-only full-text search over this tenant’s PUBLISHED knowledge-base articles (playbooks, policies, how-tos); unpublished drafts are never returned and the tenant is fixed by your credentials. Reach for this FIRST to ground an answer in official, tenant-specific guidance before replying to a customer or drafting a resolution. Returns articles ranked by relevance, each with its id, title, a highlighted snippet, and updatedAt: search uses AND semantics, so every word in the query must match. [free]

  ConnectorNo auth

• searchA

  The Chancery Lane ProjectA

  Search the TCLP knowledge graph using fusion search (semantic + BM25). Args: query: Free-text search query (max 1000 characters). node_type: Content scope — "tclp" (clauses, glossary terms, guides), "lrsf" (laws, regulations, standards, frameworks), or "all". limit: Maximum number of results to return (1–50). rerank: Whether to apply RRF reranking when combining graph and text results. 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" (totals, timing) and "results" (ranked hits with title, url, content_type, scores, and optionally relationships and full_text).

  ConnectorNo auth

• searchA

  tclp-mcpA

  Search the TCLP knowledge graph using fusion search (semantic + BM25). Args: query: Free-text search query (max 1000 characters). node_type: Content scope — "tclp" (clauses, glossary terms, guides), "lrsf" (laws, regulations, standards, frameworks), or "all". limit: Maximum number of results to return (1–50). rerank: Whether to apply RRF reranking when combining graph and text results. 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" (totals, timing) and "results" (ranked hits with title, url, content_type, scores, and optionally relationships and full_text).

  ConnectorNo auth

• preflight_bountyA

  DeskCrewA

  Free pre-check before paying to enter a bounty: is the row open and funded, does your wallet match the payout chain, do you already have a live entry there, and how much knowledge-base coverage exists to ground an answer in. Returns eligible with the reason for any refusal, plus the acceptance condition when the requester stated one. Costs nothing and changes nothing. [free]

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• generate_queryA

  ALM X++ MCP ServerA

  WHEN: developer needs correct X++ select or T-SQL for D365 tables with proper joins. Triggers: 'X++ select', 'generate a query', 'SQL for', 'join with', 'how to query', 'générer une requête', 'write a select statement', 'select from', 'X++ query for', 'requête X++', 'écrire une select'. Generate both X++ select statements and equivalent T-SQL queries for D365 F&O tables. Uses real field names, relations, and indexes from the knowledge base to produce correct joins. Supports: field selection, multi-table joins (auto-detects relations), WHERE filters, ORDER BY, TOP/firstonly, cross-company. Also accepts natural language descriptions like 'find all open sales orders for customer 1001 with CustTable join'. [!] For multi-table joins, call find_related_objects (or get_relation_graph if the relation index is loaded) FIRST to get the correct FK relations -- this tool will then produce accurate join conditions. [!] The generated X++ is a template -- adapt it to your custom code context before using in production. Returns side-by-side X++ and SQL with explanations.

  ConnectorNo auth

• sparql_queryA

  ArtsdataA

  Runs a read-only SPARQL 1.1 SELECT or ASK query against the Artsdata Knowledge Graph (https://query.artsdata.ca/query) and returns the result as SPARQL 1.1 Query Results JSON, exactly as the endpoint returns it: head.vars and results.bindings for SELECT, boolean for ASK. Call get_schema first and build the query from the classes, properties, prefixes and conventions it returns, since Artsdata's model is not general knowledge. Declare every PREFIX you use and always add a LIMIT: at most 1000 rows are returned (the rest is cut and truncated is true; page with OFFSET), and a query gets 25 seconds. If the endpoint rejects the query, its error message is returned so you can fix the query and retry. For the details of an entity URI in the results use get_entity; to find an entity's URI from its name use search_entities.

  ConnectorNo auth

• chat_knowledgeA

  AlgoVault — Crypto Quant Trade CallsA

  Returns a synthesized natural-language answer with citations, grounded in the AlgoVault knowledge bundle (every MCP tool description, response shape, integration tutorial, and code example). Use when you need an explanation, code pattern, or how-to; for raw ranked snippets without LLM synthesis use search_knowledge (faster, no quota cost). Read-only: calls an LLM, no other side effects. Quota: Free 10/month, Starter 50, Pro 200, Enterprise 2000.

  ConnectorNo auth

• query_graphA

  Gonka Second Opinion (multi-model)A

  Search Gonka documentation. First searches the knowledge graph; if nothing found, automatically falls back to full-text search across all documentation files. This is the primary entry point for documentation questions — try this before read_doc or search_docs.

  ConnectorNo auth

• list_domainsA

  mcp-serverA

  List the taxonomy domains the company has indexed — with document counts, expert counts, and coverage levels — so an agent can decide whether to query before spending a Knowledge Token. Returns one row per domain with the canonical `taxonomy_domain` slug, document/chunk counts, expert count, coverage level (expert | partial | none), the single_expert risk flag, and the top contributor by authority. Use the slug as the `domain` filter on a follow-up `query_knowledge` call. Zero Knowledge Tokens consumed.

  ConnectorNo auth

• get_source_detailA

  mcp-serverA

  Return per-chunk source provenance for a previous query — document path, lifecycle state, embedding timestamp, contributor, last-updated — useful for verifying a citation or surfacing trust signals to a downstream system. Pass a `query_id` returned by an earlier `query_knowledge` call. Returns 404 if the query_id is unknown OR belongs to a different tenant (indistinguishable to prevent info-leak). Zero Knowledge Tokens consumed.

  ConnectorNo auth

• graph_upsertA

  Moltline RecallA

  Fold new facts into a memory graph and get the whole graph back. FREE. Idempotent by construction: re-adding the same entity, observation or relation changes nothing, so an agent that replays its own history does not end up with a graph full of duplicates. Typical input {"graph": {"entities": [], "relations": []}, "entities": [{"name": "Acme Corp", "type": "company", "observations": ["renewed in March"]}]} returns {"graph": {...}, "added": {"entities": 1, "observations": 1, "relations": 0}, "merged": 0}. Use as the single write path for memory. Not for reading it back selectively - that is graph_search - and not for finding out what a write changed, which graph_diff answers precisely. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "graph exceeds <value> entities; split it"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

  ConnectorNo auth

• itinerary_quote_statusA

  AirTreks MCPA

  Fetch the result of an itinerary_quote using the quoteReference it returned. Returns status 'pending' with how long to wait, or the finished options. Costs nothing and does no searching, so poll it rather than starting a second quote.

  ConnectorNo auth