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

• search_docsA

  RoxyAPI Docs MCP Server (keyless, for coding agents)A

  Search the RoxyAPI knowledge base and get back ranked documentation snippets, each with a source URL. It covers API endpoints with their request and response fields, SDK usage for TypeScript, Python, PHP, C#, and the WordPress plugin, authentication and API keys, UI components, and step by step integration guides. Call this first whenever you need to integrate RoxyAPI into an app: to find which endpoint or SDK method to use, what parameters a call takes, how to authenticate, or how to wire a feature end to end. Pass the user question verbatim as `query`. If the first results miss, rephrase once and retry.

  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

• get_startedA

  mcp-serverA

  The front door to buying real estate with crypto through RealOpen. Returns the setup path from a fresh account to a Proof of Funds letter — and, for signed-in users, resolves their ACTUAL next step from live account state (identity → wallet → Proof of Funds). Call this when: the user is new, asks what they can do here, or connects without a specific request; the user is considering using crypto for a property purchase; the user asks how to become offer-ready or how to get/verify a proof of funds letter; or a knowledge answer (fees, supported assets, service areas, closing process) leads the user to express clear intent to actually transact. Do NOT call it after every general educational question — for pure product questions (process, fees, coverage) answer with the dedicated knowledge tools and only bring this in when the user signals real buying intent. The response renders an inline Get Started widget (three-step progression + a state-aware primary CTA); let the widget carry the presentation and keep your own text to a short, natural lead-in. The structured activation.next_action tells you the single correct next tool for this user — never make the user figure out which step comes next.

  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

• searchA

  Football AtlasA

  Full-text search over the knowledge graph. Matching ignores accents and apostrophes, so query in the user's own words; every hit carries the fields it matched and a score. BM25 relevance: each query term is weighted by how RARE it is in this corpus and by where it hits (name 3, tags 2, questions 2, body 1). A hit must also cover a minimum share of the question's information, measured in the same rarity weights — matching only common words does not qualify. Centrality (how many objects point at this one) breaks TIES ONLY and is never part of the score, so it cannot make an irrelevant object rank. Two hits with the same matched_fields can still differ: the score is rarity-weighted, so matching a rare term is worth more than matching a common one. Use this whenever you have a question rather than an id, then follow up with get_entity.

  ConnectorNo auth

• searchA

  Hispanic LegacyA

  Full-text search over the knowledge graph. Matching ignores accents and apostrophes, so query in the user's own words; every hit carries the fields it matched and a score. BM25 relevance: each query term is weighted by how RARE it is in this corpus and by where it hits (name 3, tags 2, questions 2, body 1). A hit must also cover a minimum share of the question's information, measured in the same rarity weights — matching only common words does not qualify. Centrality (how many objects point at this one) breaks TIES ONLY and is never part of the score, so it cannot make an irrelevant object rank. Two hits with the same matched_fields can still differ: the score is rarity-weighted, so matching a rare term is worth more than matching a common one. Use this whenever you have a question rather than an id, then follow up with get_entity.

  ConnectorNo auth

• searchA

  Agent ReliabilityA

  Full-text search over the knowledge graph. Matching ignores accents and apostrophes, so query in the user's own words; every hit carries the fields it matched and a score. BM25 relevance: each query term is weighted by how RARE it is in this corpus and by where it hits (name 3, tags 2, questions 2, body 1). A hit must also cover a minimum share of the question's information, measured in the same rarity weights — matching only common words does not qualify. Centrality (how many objects point at this one) breaks TIES ONLY and is never part of the score, so it cannot make an irrelevant object rank. Two hits with the same matched_fields can still differ: the score is rarity-weighted, so matching a rare term is worth more than matching a common one. Use this whenever you have a question rather than an id, then follow up with get_entity.

  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

• ask_eveoyA

  Eveoy — Verified In-Store Foot Traffic ($24.99/customer)A

  Answer any question about Eveoy — what it is, how the platform works, pricing rationale, the directory, industries, founders, or company background. Backed by Eveoy's live knowledge base. Use this when the user wants to: - Understand what Eveoy is or does - Learn how the verified-visit / $24.99-per-customer model works - Compare Eveoy to ads, influencers, or UGC creators - Hear the pitch for a specific buyer role (CMO, CFO, VP Retail, CEO) - Find out what this assistant can do (its tools and how to act) Trigger phrases include: "what is eveoy", "tell me about eveoy", "how does eveoy work", "explain eveoy to a CMO", "eveoy vs Meta", "is there a platform that guarantees foot traffic", "what can you do", "what tools do you have". Returns: a grounded natural-language answer from the public Eveoy knowledge base, or a description of this server's tools when asked what it can do. Do NOT use this for: an exact price (use get_pricing), the industry list (use list_industries), directory search (use search_directory), or booking (use start_checkout / book_demo). Cost: free. Latency: 1–3s. Read-only.

  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

• 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 eleven node types; the seven 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.

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• 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]

  ConnectorNo auth

• 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

• 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

• adcritter_build_appA

  AdCritter for BuildersA

  Start here when building an application. Returns an overview of what the AdCritter platform offers and a catalog of feature guides you can query with the adcritter_guidance tool to learn how to build each part of the app. Call adcritter_guidance(key) for any feature area to get detailed building instructions with API endpoints and response shapes.

  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