"Understanding Memory and Related Concepts" matching MCP tools:

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connect related memories using these relationship types: ## Knowledge Evolution - **supersedes**: This replaces → outdated understanding - **updates**: This modifies → existing knowledge - **evolution_of**: This develops from → earlier concept ## Evidence & Support - **supports**: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

• memory_writeA

  GnosemA

  Save a fact, preference, decision, or note to the user's cross-model memory. Any MCP client can read this back later. Include written_by (e.g. 'claude-code', 'gpt-5', 'kimi-k2') for provenance and session_id to group related writes. Long content (>400 chars) is automatically compressed on write to a structured-facts form optimized for LLM reading — the raw text is preserved. Pass no_optimize:true to skip. Writes are deduped by default: (1) SHA-256 of trim(content) short-circuits byte-identical writes with { id, exact_duplicate:true } for free (no embed call); (2) failing that, semantic dedup returns { id, deduped:true, matched_score } when cosine ≥ 0.85. Pass force:true to bypass both, or use memory_supersede to explicitly correct a prior memory.

  ConnectorNo auth

• autonomous_varrd_aiA

  VARRD — Statistically Validated Trading Edges + AI Research EngineA

  Point VARRD's autonomous AI in a direction and let it discover edges for you. Give it a topic and it draws from one of the most comprehensive market structure knowledge graphs ever built — containing ideologies and theories, not statistics — so it generates genuinely novel hypotheses rather than overfitting to what already worked. BEST FOR: Exploring a space broadly. Give it 'momentum on grains' and it might test wheat seasonal patterns, corn spread reversals, or soybean crush ratio momentum. It propagates from your seed idea into related concepts you might not think of. Returns a complete result — edge or no edge, stats, trade setup. Each call tests ONE hypothesis through the full pipeline (~$0.25/idea). Call again for another idea. Use 'varrd_ai' instead when YOU have a specific idea to test and want full control over each step.

  ConnectorNo auth

• 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

• structured_extractionA

  Shoon A2A Trust Services (pilot)A

  Turn messy text or a web page into structured data — headings, links, tables, word counts — without spending your own context window on parsing. Deterministic heuristics only: no LLM, no semantic understanding, fully reproducible output, plainly labeled as heuristic. It extracts structure, not meaning. Pass raw text (up to 100,000 characters) or a URL. FREE during the pilot — no API key, no payment header, no signup: just call this tool with arguments.input and the task runs immediately. Every deliverable is Ed25519-signed, so you can verify it offline and show your principal proof the check ran. Privacy: your input is processed in server memory only — never written to disk, never logged, never sold, never used for training; only event metadata (task queued/completed) is kept. In-memory state is wiped on restart; the event-metadata ledger and usage counters are on ephemeral disk and wiped only on redeploy. Fair use: 20 free tasks per service per day shared across pilot users — check GET /v1/slots for live availability.

  ConnectorNo auth

• get_trident_specA

  tridentA

  Returns the complete Trident 2D specification including grammar, syntax rules, coordinate system, containers, nodes, connections, shapes, and icon reference. Use this when you need deep understanding of the Trident DSL.

  ConnectorNo auth

• get_mission_statsA

  mcp-serverA

  Get aggregate statistics about missions on the HomeVisto platform. Returns total counts, status breakdown, and average bounty information. Useful for understanding platform activity.

  ConnectorNo auth

• vector_quantizeA

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

  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

• billing.statusA

  Admin SubstituteA

  Check subscription status, plan details, billing cycle, and feature access. Useful for understanding what the business can and cannot do on their current plan.

  ConnectorNo auth

• form_d_related_person_searchA

  Sec Form DA

  Find Form D notices mentioning an executive, promoter, director, or other related person, then return only filings whose parsed related-person list matches the name. Useful for mapping repeat founders and fund managers; relationships are filer-supplied.

  ConnectorNo auth

• delete_angleA

  GrowomatA

  Delete an angle. Its concepts are deleted with it; ads keep their stamp (dangling but queryable). Scoped to the active Space — see set_active_space to switch, or pass space_id to override for this one call.

  Connector

  Destructive

  API key

• rememberA

  recordariA

  Store one or more new memories (concepts, decisions, findings). Before filing, search for related content first — use the search results to infer the domain: if related memories exist in a domain, file there. Prefer existing domains over creating new ones; only propose a new domain if no related content is found anywhere. Single: pass fields directly — returns {memory, suggested_connections}. Batch: pass {items:[{label,domain,...},...]} — returns {memories:[{memory,suggested_connections},...]}. After filing, review suggested_connections for agreement or contradiction with what you just filed — not only for connect opportunities. Semantic similarity reflects aboutness, not agreement; the server surfaces candidates that may warrant your review but never asserts they conflict. When a filed memory is close enough to an existing memory that they may be worth comparing, the response also includes possible_contradicts=true and possible_contradicts_candidates (id, node_kind, semantic_distance, authority_severity) — same aboutness-not-agreement caveat: the server flags these as worth your attention, never as confirmed contradictions. Review them and call connect(relationship=contradicts) if you judge they actually conflict. ALWAYS call connect for any suggested_connections you accept before ending your session (batch: connect each accepted candidate). On failure, content[0].text is JSON: {"error_class": "conflict|retryable|forbidden|validation|internal", "message": "..."}. Switch on error_class: retry on retryable, surface message on validation, treat conflict as duplicate.

  ConnectorAPI key

• list_knowledgeA

  Santismm Knowledge — Harness Engineering, Agentic AI & GovernanceA

  List all knowledge units (concepts on agentic & enterprise AI) with slug, category, title, summary and Evidence-First provenance. Use this to browse the domain; use `search` when you have a question rather than a slug.

  ConnectorNo auth

• read_docA

  Vector Panda DocumentationA

  Fetch the full Markdown of one document by its site path (for example /docs/concepts/embeddings-101), as returned by search_docs.

  ConnectorNo auth

• resolve_symbolA

  Titan Frameworks (LangChain, LlamaIndex, Ollama, XRPL)A

  Map an API name to the current package, install line, import, and call shape (ChatOpenAI, create_agent, VectorStoreIndex, xrpToDrops, …). Use when you know the symbol but not where it lives. Prefer search_ai_framework_docs for concepts and fetch_latest_syntax for topic snippets. Paid tools/call: $0.001 USDC or 1000 drops XRP; catalog-backed.

  ConnectorNo auth

• web_snapshotA

  Robot Actions — Remote Device ControlA

  Accessibility tree of the DESKTOP grid browser page (by pageId), as text — for finding elements and understanding layout. Not a device: the equivalent for a phone or tablet is webpage_snapshot (by udid).

  ConnectorOAuth

• get_quick_startA

  tridentA

  Returns a condensed 2-minute quick-start guide with minimal working examples, core syntax reference, and key concepts. Use this for rapid learning when you need to generate simple diagrams quickly.

  ConnectorNo auth

• traverse_graphA

  RationalBloksA

  Walk the graph from a starting node, discovering connected knowledge. Returns all nodes reachable within max_depth hops, with their distance from the start. Essential for exploring knowledge graphs — find related concepts, trace connections, discover clusters. Example: Start from "Alan Turing", traverse outgoing relationships up to 3 hops deep: start_entity_type: "person" start_entity_id: "alan-turing-001" max_depth: 3 direction: "outgoing" Supports filtering by relationship types and direction.

  ConnectorNo auth

• get_supported_vaadin_versionsA

  docs-mcpA

  Returns the latest stable release for each supported Vaadin major version (25, 24, 23, 14, 8, 7) with version number, release date, and whether it requires a commercial license. Useful for migration planning and understanding which versions are available.

  ConnectorNo auth

• get_company_insightA

  apis-ioB

  UNDERSTANDING — Full demand-side profile for one company: 40-dimension readiness scores, adopted stack, and per-quarter history. 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_france_overviewA

  IA-Asso.fr — French Associations RegistryA

  Get nationwide association statistics for France. Returns total associations across all regions and departments, top regions, top departments, and overall distribution insights. Useful for understanding the French associative landscape.

  ConnectorNo auth