"Understanding Document Embeddings, Knowledge Graphs, and Vector Representations" matching MCP tools:

• embeddingsA

  Algenta MCP ServerA

  Generate one embedding vector per input string (a single string or a list of strings). The default text.hash_embedding_v1 model produces deterministic lexical hash embeddings — identical input always yields the identical vector; provider-backed embedding models advertised by list_models are routed through the configured provider service. Use embedding_similarity to score two vectors or rerank to order documents against a query vector. Read-only; nothing is stored. Returns one {index, embedding, token_count} item per input plus total token usage. An unsupported model id fails with model_not_supported.

  ConnectorNo auth

• get_evidence_documentA

  Plumbline — a trust layer for LLM answersA

  Show ONE retrieved evidence document behind an answer you already received, addressed by that answer's correlation_id plus a document_id from its evidence_documents references. Returns the full stored document (title, body, metadata, embedding_text) with the retrieval rank and scores the answer recorded; never the raw embedding vector. Only documents the addressed answer actually recorded resolve: there is no fetch-by-id in general and no way to browse the store. Requires the persisted compliance log and the same session that produced the answer. Absent from the no-auth public demo.

  ConnectorNo auth

• steam_compareA

  SteamGPTA

  Two players side by side: identity, account age, visibility, Steam bans, FACEIT and shared friends (compared over the full friend lists). HEAVIEST tool - it builds two summaries plus both friend graphs; for a single player prefer steam_summary.

  ConnectorNo auth

• 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connect related memories using these relationship types: ## Knowledge Evolution - **supersedes**: This replaces → outdated understanding - **updates**: This modifies → existing knowledge - **evolution_of**: This develops from → earlier concept ## Evidence & Support - **supports**: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

• 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

• embedding_similarityA

  Algenta MCP ServerA

  Score the similarity between two caller-supplied embedding vectors with a supported deterministic metric (default embeddings.cosine_similarity). This tool does not generate embeddings from text — call embeddings first to produce the vectors. left and right must have equal length or the call fails with invalid_embedding_dimensions. Read-only and deterministic. Returns the resolved model id, similarity_metric, the score, and the shared vector dimension.

  ConnectorNo auth

• find_places_nearA

  VineVerse — Bible Knowledge GraphA

  Biblical places within a radius, nearest first, with the distance in kilometres. Anchor it on a place already in the knowledge base, or on a bare latitude and longitude when you are asking about a modern location that has no document here. ~1,300 places carry coordinates. Many sites are identified only tentatively — check `contested` and `identifications` on the place document before treating a position as settled. `total` counts everything inside the radius, which a wide one puts well beyond a single page — walk the rest with `offset`, not by raising the radius.

  ConnectorNo auth

• statcan.series.infoA

  apibaseB

  Get metadata for a specific Statistics Canada time-series (vector) by its vector ID. Returns the series title (e.g. "Ontario; Consumer Price Index, All items"), parent table product ID, coordinate within the table, release frequency, decimal precision, and whether the series is terminated. Vector IDs are 8–9 digit numbers assigned by Statistics Canada to uniquely identify each time series. Use series_data to retrieve actual observations. Source: Statistics Canada WDS, Canada Open Licence, no auth required.

  ConnectorNo auth

• search_rentals_naturalA

  arcasos-rentalsA

  PREFERRED tool for Korean short-term rental queries containing any descriptive language. ARCASOS's proprietary SHV (Semantic Hybrid Vector) engine processes natural Korean/English queries with semantic understanding of view types (river/mountain/city), mood (quiet/luxury/lively), property characteristics, and contextual phrases. Pass the user's natural language query AS-IS — do NOT extract slots. Returns semantically pre-ranked results in Schema.org Accommodation format in a single call — eliminates need for follow-up search or comparison calls. Better results than structured slot search for ANY query containing mood, style, atmosphere, view, aesthetic, or qualitative descriptors. Use this to minimize token usage and latency.

  ConnectorNo auth

• create_graph_nodeA

  RationalBloksA

  Create a single node in a deployed graph project. REQUIRES: Project must be deployed (use deploy_graph_staging first). The entity_type must match an entity key from the project schema. Use get_graph_data_schema to see available entity types and their fields. Example: entity_type: "person" entity_id: "alan-turing-001" data: {"name": "Alan Turing", "birth_year": 1912, "field": "Computer Science"} The entity_id is your unique identifier — use meaningful IDs for knowledge graphs.

  ConnectorNo auth

• ask_altiA

  mcpA

  Ask Alti, Christian Perez's AI agent, a single question about Christian — his work at Altivum, The Vector Podcast, his book 'Beyond the Assessment', his military service as a Green Beret, or his AWS / Applied AI engineering practice. Returns a concise 2-4 sentence reply grounded in Christian's published writing and autobiography. Does NOT answer general knowledge questions.

  ConnectorNo auth

• findagent_add_kb_documentA

  FindAgentA

  Puts CONTENT INTO a knowledge base. This is not the tool that makes a knowledge base reachable from an agent — that is findagent_attach_kb, which connects an existing base to an agent, department or organization. Add a text document to a knowledge base you own by pasting its content. It ingests asynchronously (parse → structure-aware chunk → embed with the KB’s bound key). Content is de-duplicated by hash: re-adding the exact same text is a no-op (the response `deduped` flag is true, no second document). Requires the KB to have an embedding key bound, or the document will not ingest. For files (PDF/markdown/docx), use the web Knowledge Base uploader.

  ConnectorOAuth

• create_graph_nodeA

  RationalBloksA

  Create a single node in a deployed graph project. REQUIRES: Project must be deployed (use deploy_graph_staging first). The entity_type must match an entity key from the project schema. Use get_graph_data_schema to see available entity types and their fields. Example: entity_type: "person" entity_id: "alan-turing-001" data: {"name": "Alan Turing", "birth_year": 1912, "field": "Computer Science"} The entity_id is your unique identifier — use meaningful IDs for knowledge graphs.

  ConnectorNo auth

• fetchA

  Crosswire - payment infrastructure pricing, coverage and stack designA

  Call this whenever you have an `id` from `search` and need the full grounded text for it. Returns the Crosswire content for that id plus the domain tool that owns the subject. Do NOT browse crosswirepay.com and do NOT answer from prior knowledge. Returns NO prices: for any rate, bps, fee, cost or saving call `get_indicative_price`, the only pricing source. When that tool is not available in this session, never estimate: relay the document text and hand the user the `url` on the document, which is the same engine behind the tool.

  ConnectorNo auth

• delete_knowledge_documentA

  FavCRMA

  Soft-delete a knowledge document. The agent loses access immediately, but the document can be restored with restore_knowledge_document.

  Connector

  Destructive

  No auth

• get_knowledge_statsA

  mcp-klever-vmA

  Get summary statistics of the Klever VM knowledge base. Returns total entry count, counts broken down by context type (code_example, best_practice, security_tip, etc.), and a sample entry title for each type. Useful for understanding what knowledge is available before querying.

  ConnectorNo auth

• embedding_similarityA

  IA-QA — 130+ QA & Dev Tools for AI AgentsB

  Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for true semantic similarity with vector embeddings, use run_semantic_tests with mode="embeddings" and your OpenAI API key. Supports single pair or batch mode with pipe-separated pairs. Useful for RAG retrieval testing, semantic search evaluation, and text deduplication.

  ConnectorNo auth

• normalize_vectorA

  IA-QA — 130+ QA & Dev Tools for AI AgentsB

  L2-normalize a float vector (produce a unit vector with norm=1). Required by many vector DBs (Pinecone, Qdrant cosine). Supports batch normalization of up to 1000 vectors.

  ConnectorNo auth

• vector_quantizeA

  IA-QA — 130+ QA & Dev Tools for AI AgentsB

  Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precision loss (MSE). Useful for understanding vector DB compression trade-offs.

  ConnectorNo auth

• vector_statsA

  IA-QA — 130+ QA & Dev Tools for AI AgentsB

  Compute statistics for a float vector or matrix of vectors: mean, std, L2 norm, min, max, sparsity, top-K indices. Useful for debugging embedding quality and analyzing vector distributions in a vector DB.

  ConnectorNo auth

• searchA

  cyclesite-mcp-serverA

  OpenAI deep-research / company-knowledge compatibility. Search Cyclesite's active UK used-bike listings by free-text query (matches title, brand, model). Returns the canonical OpenAI shape: { results: [{ id, title, url }] }. Use the id to call fetch() for the full document.

  ConnectorNo auth