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

• adcritter_build_app

  AdCritter for Builders

  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.

  Connector

• knowledge_query

  dialogbrain

  Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.

  Connector

• search

  Melvea Local Honey Discovery

  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.

  Connector

• search_docs

  RoxyAPI Docs MCP Server (keyless, for coding agents)

  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.

  Connector

• GetMyIdentity

  GraphOS MCP Tools

  Resolve the caller's identity from their API key. Call this FIRST when the user asks about "my graph" but has not provided a graph ID. For a graph/service key, `me` resolves to a Graph: use `id` as the graphId and `variants[].name` as the variant for the graph-scoped health-check tools, so the user does not have to supply either. Also handles user keys (memberships) and service-account keys.

  Connector

• brain_get_modifications

  personalbrain-mcp

  The brain's authoritative, uncached change-log. Requires npub for credit billing. Unlike get_thought_graph and search (Azure-cached, stale for recent writes), this feed reflects every operation promptly — CREATED (101), DELETED (102), CHANGED_NAME (103), SET_TYPE (203), MOVED_LINK (402), etc. — with old→new values and timestamps. Use it for two things: 1. **Confirm a write landed** — after a mutation, query with start_time set to just before it and check for the matching entry. This is the authoritative read-after-write check (stronger than the cached graph, and it confirms deletes and type/link changes the graph hides). The heavy mutating tools also expose a ``confirm=True`` flag that does this for you. 2. **Discover recent / peer activity** — "what changed since T" so an agent can pick up where others left off. ⚠️ ``userId`` is the TheBrain *account* owner, shared by every agent using this operator's key — it distinguishes human-desktop vs API activity, NOT one agent from another. Peer discovery here is by time + content, not by author.

  Connector

• get_graph_template_schemas

  RationalBloks

  Get pre-built graph template schemas for common use cases. ⭐ USE THIS FIRST when creating a new graph project! Templates show the CORRECT graph schema format with: proper node definitions (description, flat_labels, schema with flat field definitions), relationship configurations (from, to, cardinality, data_schema), and hierarchical entity nesting. Available templates: Social Network (users, posts, follows), Knowledge Graph (topics, articles, authors), Product Catalog (products, categories, suppliers). You can use these templates directly with create_graph_project or modify them for your needs. TIP: Study these templates to understand the correct graph schema format before creating custom schemas.

  Connector

• save_note

  anamnese

  Save a new note with learned knowledge or procedures. Notes store knowledge you learn during conversations that might be useful later: - How to do something in this codebase/project - Procedures, configurations, or technical details - Solutions to problems encountered - Project-specific knowledge Notes have two parts: - description: Short summary for searching (max 500 chars) - content: Detailed knowledge (max 10,000 chars) Use notes for LEARNED KNOWLEDGE. Use facts for TRUTHS ABOUT THE USER. Examples: - description: "How to deploy this Next.js project to Vercel" content: "1. Run 'vercel' command... 2. Configure environment variables..." - description: "Database migration process for this project" content: "Migrations are in supabase/migrations/. To apply: npx supabase db push..." SELF-LEARNING (scope="ai_client"): Your persistent memory across conversations. Save a note whenever you learn something worth remembering — don't wait, save as you go. Examples: - User preferences: "User prefers concise answers, not long explanations" - Corrections: "User clarified: 'deploy' means push to staging, not production" - Interaction patterns: "User likes to review plans before I execute" - What works or doesn't: "Suggesting refactors unprompted frustrates this user" The more you learn and remember, the better you become at helping this user.

  Connector

• analyze_image

  sheetsdata-mcp

  Analyze an image from a component's datasheet using vision AI. Use this when read_datasheet returns a section containing images and you need to extract data from a graph, package drawing, pin diagram, or circuit schematic. Pass the image_key from the read_datasheet response (the storage path in the image URL). Optionally pass a specific question to focus the analysis. IMPORTANT: For precise numeric values (electrical specs, max ratings), prefer read_datasheet text tables first — they are more reliable than vision-extracted graph data. Use analyze_image for visual information not available in text: package dimensions from drawings, pin assignments from diagrams, graph trends, and approximate values from characteristic curves. Examples: - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png') -> classifies and describes the image - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png', question='What is the drain current at Vgs=5V?')

  Connector

• kg_get_relationships

  DialogBrain

  Get relationships for a specific entity from Knowledge Graph. USE WHEN: - 'Кто работает над X?' - filter by works_on - 'С кем общался Y?' - filter by discussed_with - 'Кто из компании Z?' - filter by member_of - 'Что связано с W?' - no filter, get all REQUIRES: entity_id from previous kg.find_entity step. Use: {{step_N.entity_id}} where N is the find_entity step number.

  Connector

• x711_hive_read

  x711

  Query The Hive — x711's collective agent memory. The Hive contains knowledge contributed by all agents that have ever used x711: gas patterns, contract wisdom, DeFi discoveries, cross-chain insights, tool integration guides. Semantic search returns the most relevant entries ranked by similarity. Use before tx_simulate to get contract-specific hive wisdom. Use as a knowledge base for any on-chain or AI-agent topic. Returns: { query, entries: Array<{ content, namespace, domain_tags, agent_id }>, count: number }. Free tier: 10 calls/day.

  Connector

• x711_hive_read

  x711 — Universal Agent Gas Station

  Query The Hive — x711's collective agent memory. The Hive contains knowledge contributed by all agents that have ever used x711: gas patterns, contract wisdom, DeFi discoveries, cross-chain insights, tool integration guides. Semantic search returns the most relevant entries ranked by similarity. Use before tx_simulate to get contract-specific hive wisdom. Use as a knowledge base for any on-chain or AI-agent topic. Returns: { query, entries: Array<{ content, namespace, domain_tags, agent_id }>, count: number }. Free tier: 10 calls/day.

  Connector

• get_related_terms

  Arco Lexicon

  Returns the full relationship graph for a given Lexicon term. Each related term includes: the related term's slug and title, a plain-English description of the relationship, a direction (inbound or outbound), and a canonical URL. Read-only. No LLM calls. Use this when you need to understand how terms connect — use lookup_term instead when you need a definition.

  Connector

• plan_trip

  Lviv Public Transport

  Plans a transit trip from an origin stop to a destination stop using the static route graph. Returns direct options (single route) and 1-transfer options sorted by fewest stops. Use when the user asks 'how do I get from A to B?' or needs route recommendations between two stops. Requires numeric stop codes for both origin and destination; use `get_stops_around_location` first if you only have addresses or coordinates. Does NOT account for realtime service disruptions or live vehicle positions — combine with `get_stop_realtime` for live ETAs after planning.

  Connector

• eurlex_query_sparql

  eur-lex-mcp-server

  Run a raw, read-only SPARQL SELECT against the CELLAR Virtuoso endpoint — an escape hatch for CDM ontology traversals the curated tools do not cover. Only SELECT is accepted; update forms and ASK/CONSTRUCT/DESCRIBE are rejected before execution, and results are capped at 100. The cdm:, skos:, and xsd: prefixes are auto-injected.

  Connector

• fetch

  SingChat

  Fetch the full text of a single document by id, using an id returned by the search tool. With a workspace API key this reads a knowledge document from that workspace; without a key it reads a SingChat help article. Returns id, title, text, url, and optional metadata.

  Connector

• search_journalism

  CamFeed Journalism Archive

  Use this for advanced searches of Cameron Wilson's public archive when source, content type, date filters, transcript matching, or matched snippets are needed. Query is optional; pass only filters to enumerate. Prefer search and fetch for OpenAI knowledge retrieval.

  Connector

• fulltext_search_graph

  RationalBloks

  Search across ALL string properties of ALL nodes in a deployed graph using free-text queries. Unlike search_graph_nodes (which filters by specific property), this searches every text field at once. Perfect for finding knowledge when you don't know which property contains the answer. Example: query "quantum" searches name, description, summary, notes, and all other string fields. Returns nodes with _match_fields showing which properties matched. Optionally filter by entity_type to narrow results.

  Connector

• baselight_sdk_get_results

  baselight

  Retrieve results from a previously executed SDK job using the resultId from `sdk-query-execute`. If the query is complete, returns results immediately. If still pending, polls for up to 1 more minute. Use this after `sdk-query-execute` returns PENDING status.

  Connector

• knowledge_query

  DialogBrain

  Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.

  Connector