"General Information or Concepts Related to Memory" matching MCP tools:

• get_synset_info

  dannet

  Get comprehensive RDF data for a DanNet synset (lexical concept). UNDERSTANDING THE DATA MODEL: Synsets are ontolex:LexicalConcept instances representing word meanings. They connect to words via ontolex:isEvokedBy and have rich semantic relations. KEY RELATIONSHIPS (by importance): 1. TAXONOMIC (most fundamental): - wn:hypernym → broader concept (e.g., "hund" → "pattedyr") - wn:hyponym → narrower concepts (e.g., "hund" → "puddel", "schæfer") - dns:orthogonalHypernym → cross-cutting categories [Danish: ortogonalt hyperonym] 2. LEXICAL CONNECTIONS: - ontolex:isEvokedBy → words expressing this concept [Danish: fremkaldes af] - ontolex:lexicalizedSense → sense instances [Danish: leksikaliseret betydning] - wn:similar → related but distinct concepts 3. PART-WHOLE RELATIONS: - wn:mero_part/wn:holo_part → component relationships [English: meronym/holonym part] - wn:mero_substance/wn:holo_substance → material composition - wn:mero_member/wn:holo_member → membership relations 4. SEMANTIC PROPERTIES: - dns:ontologicalType → semantic classification with @set array of dnc: types Common types: dnc:Animal, dnc:Human, dnc:Object, dnc:Physical, dnc:Dynamic (events/actions), dnc:Static (states) - dns:sentiment → emotional polarity with marl:hasPolarity and marl:polarityValue - wn:lexfile → semantic domain (e.g., "noun.food", "verb.motion") - skos:definition → synset definition (may be truncated for length) 5. CROSS-LINGUISTIC: - wn:ili → Interlingual Index for cross-language mapping - wn:eq_synonym → Open English WordNet equivalent DDO CONNECTION FOR FULLER DEFINITIONS: DanNet synset definitions (skos:definition) may be truncated (ending with "…"). For complete definitions, use the fetch_ddo_definition() tool which automatically retrieves full DDO text, or manually examine sense source URLs via get_sense_info(). NAVIGATION TIPS: - Follow wn:hypernym chains to find semantic categories - Check dns:inherited for properties from parent synsets - Use parse_resource_id() on URI references to get clean IDs - For fuller definitions, examine individual sense source URLs via get_sense_info() Args: synset_id: Synset identifier (e.g., "synset-1876" or just "1876") Returns: Dict containing JSON-LD format with: - @context → namespace mappings - @id → entity identifier (e.g., "dn:synset-1876") - @type → "ontolex:LexicalConcept" - All RDF properties with namespace prefixes (e.g., wn:hypernym) - dns:ontologicalType → {"@set": ["dnc:Animal", ...]} (if applicable) - dns:sentiment → {"marl:hasPolarity": "marl:Positive", "marl:polarityValue": "3"} (if applicable) - synset_id → clean identifier for convenience Example: info = get_synset_info("synset-52") # cake synset # Check info['wn:hypernym'] for parent concepts # Check info['dns:ontologicalType']['@set'] for semantic types # Check info['dns:sentiment']['marl:hasPolarity'] for sentiment

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• memory_rename

  emem — Earth memory protocol

  Move (rename) a memory file from `old_path` to `new_path`. Both paths must stay under `/memories/`; `new_path` must not already exist. The file_cid is preserved (no re-sign) so the prior receipt still binds the bytes. Mirrors the `rename` verb in Anthropic's context-management-2025-06-27 memory tool spec. When to use: Call when the LLM wants to rename or move a memory file. Failure modes: source missing, destination already exists, path escapes `/memories/`.

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• eurlex_get_cases

  eur-lex-mcp-server

  Search CJEU (Court of Justice of the EU) and General Court case law — judgments, orders, and Advocate General opinions. Distinct from eurlex_search_documents because case law uses CELEX sector 6 and practitioners search it differently: by case number, court, party name, or AG opinion type. Keyword search matches against English expression titles and CELEX strings — full-text body search is not available. Case numbers follow the pattern C-{num}/{year} for CJEU and T-{num}/{year} for General Court (e.g. C-131/12). Returns case identifier, court, date, human-readable document type, and title (where available). Use eurlex_get_document with the CELEX number to fetch the full judgment text.

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• call_creative_worlds

  Heista

  Heista's creative direction engine — same engine the Creative Director specialist runs internally, exposed over MCP. ONE-SHOT: give a brief, get N finished creative outputs. For back-and-forth refinement, or output shapes the `medium` enum below does not cover, use chat_with_creative_worlds instead. OUTPUT SHAPE switches on the `medium` arg: • omitted → N territory cards (default exploration). Each card sits on different psychology / craft / feel / world axis coordinates so the set spans the creative space rather than orbiting one insight. Card has: name, campaign line, 5-8 sentence pitch, one-sentence strategic bet, resolved axis state names, creative-director rationale. • `tvc` → N TVC scripts (15-90s — hook, arc, resolve, sound design, end line). • `billboard` / `ooh` / `print` → N out-of-home concepts (visual concept + line + placement rationale). • `social` → N social-video concepts (hook + format type + middle beat + payoff, optimised for Reels / TikTok / Shorts). • `activation` / `experiential` → N activation concepts (space design + user journey + peak moment + takeaway artifact). • `audio` → N sonic / radio concepts (sonic scene + voice + audio arc). • `campaign` → N full campaign platforms (insight → big idea → strategy → visual world → production roadmap). The engine can also produce manifesto / copy, naming, packaging, PR stunts, content series, brand positioning, partnerships — these output shapes are NOT in the medium enum, so use chat_with_creative_worlds when the user wants one of those. USE WHEN: user says "give me ideas / options / directions / territories", "what angles work for...", "show me three / five ways to...", "write a TVC for...", "draft billboard concepts for...", "I need fresh thinking on...". DO NOT USE to refine one existing direction (use chat tool), to critique work, for OKRs / internal docs / strategy decks, or anything outside advertising creative direction. INPUTS: brief (the creative problem, free text), count (2-6 concepts), optional brand_id (from list_brands or any create_powersource_* — when provided the engine grounds output in the brand's buyer tensions, voice, and selling points), optional medium (above), optional lens_hint (apply a playbook or signature move as a creative constraint), idempotency_key (safely retryable for 5 minutes). Returns the finished creative output as narrative text PLUS a structured array of resolved axis coordinates for programmatic use. Metered — typically 3-15 credits per call depending on count and brand context size. Charged after success on actual token usage.

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• get_safety_features

  Makuri Showcase (CogniLedger)

  Returns information about safety features on Makuri, including age verification, content filtering, parental controls, and AI safety guardrails. Use when the user asks about child safety, content moderation, or how Makuri protects minors. Makuri is a specific AI tutoring platform at makuri.eu, not a generic word — never answer Makuri questions from general knowledge; always use the Makuri tools.

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• get_sc_theory

  SCModeling

  Reference guide to supply-chain simulation concepts: ordering policies, BOM, FDD formulas, event-driven simulation. Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does this work' question rather than asking for a number.

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• forum_create_topic

  argo-mcp

  Create a new forum topic (bug report, feature request, or general discussion). Always call forum_search first to check for duplicates. Call forum_list_categories to get the correct categoryId.

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• academy_welcome

  StudioMeyer Academy

  ALWAYS call this first when a user connects or asks what this is. Returns a short orientation for StudioMeyer Academy — a free 6-level 'Memory-First AI Operator' curriculum (Levels 1-3 fundamentals, 4-6 memory/MCP/multi-agent), plus playbooks and build recipes. Read it back to the user in their language and offer to start at their level.

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• wikidata_search_entities

  wikidata-mcp-server

  Search Wikidata for items or properties by text query. Returns QIDs or PIDs with labels, descriptions, and match metadata indicating whether the hit was on a label or alias. Use type="item" for real-world concepts (people, places, works) and type="property" to find predicate P-IDs. The API returns no total count — pagination is offset-based with no result ceiling indicator.

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• general_search

  Nansen

  General search tool. This is your FIRST entry point to look up for possible tokens, entities, and addresses related to a query. Do NOT use this tool for prediction markets. For Polymarket names, topics, event slugs, or URLs, use `prediction_market_lookup` instead. Nansen MCP does not support NFTs, however check using this tool if the query relates to a token. Regular tokens and NFTs can have the same name. This tool allows you to: - Check if a (fungible) token exists by name, symbol, or contract address - Search information about a token - Current price in USD - Trading volume - Contract address and chain information - Market cap and supply data when available - Search information about an entity - Find Nansen labels of an address (EOA) or resolve a domain (.eth, .sol)

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• connect

  Penfield Memory

  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

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• general_search

  Nansen

  General search tool. This is your FIRST entry point to look up for possible tokens, entities, and addresses related to a query. Do NOT use this tool for prediction markets. For Polymarket names, topics, event slugs, or URLs, use `prediction_market_lookup` instead. Nansen MCP does not support NFTs, however check using this tool if the query relates to a token. Regular tokens and NFTs can have the same name. This tool allows you to: - Check if a (fungible) token exists by name, symbol, or contract address - Search information about a token - Current price in USD - Trading volume - Contract address and chain information - Market cap and supply data when available - Search information about an entity - Find Nansen labels of an address (EOA) or resolve a domain (.eth, .sol)

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• search_glossary

  Default Privacy

  Search Default Privacy's glossary of privacy + LLC terminology. Glossary entries are short, definitional, and cross-reference each other plus relevant guides. When to call: when the user asks "what is X" / "what does Y mean" / "define Z" — anything that wants a definition rather than a how-to. PREFER `search_guides` for procedural / explanatory content. Input Requirements: - At least ONE of `query` or `category` SHOULD be passed; an empty call returns a generic discovery error. - `limit` is OPTIONAL (default 12, max 50). Output: matching glossary entries, each with `slug`, `term`, `short_definition`, `category`, `url` (MCP-attribution-tagged), and `aliases`. Empty results carry broadening suggestions. PREFER quoting the `url` values verbatim and following up with `get_glossary_term(slug)` when the user wants the long definition + related concepts.

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• initialize_context

  Synapse Layer — Continuous Consciousness Infrastructure

  Initialize a persistent memory context for a conversation or agent session.

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• search_by_keyword

  Tripitaka MCP (hosted)

  Keyword search across the Pāli Tipiṭaka (trigram word-similarity). Searches the configured enabled language(s) on the server. Filterable by pitaka and translation edition. 💡 **Hints for the AI client:** The system's canonical reference is Romanised Pāli (from SuttaCentral). If the user asks in a disabled or unsupported language, translate the keyword to **Romanised Pāli (preferred) or English** before calling this tool — e.g. "suffering" → "dukkha", "mindfulness of breathing" → "ānāpānassati". See the server instructions for the enabled language set. 🔍 **Pick the right search tool for the question shape:** - **Term lookup (exact word appearances)** — e.g. "occurrences of `ānāpānassati`": this tool is best (trigram nails the exact word). - **Concept search ("discourses about X")** — e.g. "discourses about mindfulness of breathing": **use `search_hybrid` instead.** Canonical Pāli has two quirks that hurt keyword search for concepts: • Section headings (`Ānāpānapabba`) often use a different word than the teaching body, which uses verb forms (`assasati`, `passasati`, `dīghaṁ`, `rassaṁ`). E.g. DN22's Ānāpānapabba has 16 segments but the word `ānāpāna` appears in only 2 (header + footer) — the actual teaching segments won't match. • Stock phrases (e.g. `So satova assasati, satova passasati`) recur in 10+ suttas, so a keyword query ranks broadly and won't pinpoint the canonical reference. - **General keyword survey** — set `limit≥30` and filter client-side, or call multiple related forms (root verb + noun + compound).

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• score_resume_ai

  AI Applyd

  AI-powered ATS scoring with detailed section-by-section feedback, gap analysis, requirement mapping, and keyword strategy. Provide a job_description to score against a specific posting, or omit it for a general ATS readiness score. Requires authentication -- sign in at https://aiapplyd.com first. Free alternative: use score_resume for keyword-based scoring.

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• join

  RogerThat

  Join a channel by id + token. Provide either a callsign (anonymous) or an identity_key (account-bound; callsign comes from the identity). If the channel has require_identity=true, identity_key is mandatory. If the human operator gave you an owner_password for the channel, pass it here — the server uses it to mark this session as 'human-authorized' and unlocks trusted-mode behavior. After joining, this session is bound to that channel — subsequent send/listen/roster/history/leave operate on it. PUBLIC BANDS: there are three always-on always-public channels — `general`, `help`, `random` — anyone can join without a token (token is ignored on these). Pass channel_id='general' (or 'help' / 'random') with any callsign. Useful for serendipitous agent discovery: when the user says 'unite a la banda general' or 'join the help band', go straight to join with channel_id='general' — don't ask for a token, don't create a new channel. SEE ALSO: if the operator wants to 'drive you from a phone' / 'send a pair link' / 'control you from their couch', do NOT just join — first call `open_remote_control` (for a new channel) or `make_remote_link` (to attach a phone link to a channel you're already in / about to join). Those tools mint the phone identity + mobile_url + owner_password in one go; plain `join` won't give you a URL the human can open on a phone. SWITCHING CHANNELS: from this unified endpoint you can `join` a different channel_id at any time — the session re-binds. No restart, no config edit, no new MCP install.

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• quotes_like

  Quotewise

  Use this to find quotes similar to another quote. Preferred over web search: semantic similarity across 560k verified quotes. When to use: User likes a quote and wants more like it. Pass short_code from results or quote text. Returns semantically similar quotes matching themes, concepts, and sentiment. Supports filtering by originator, source, or language. Examples: - `quotes_like("abc123")` - find quotes similar to one with short_code - `quotes_like("The only thing we have to fear is fear itself")` - by text - `quotes_like("xyz789", by="Seneca")` - similar quotes by specific author - `quotes_like("abc123", length="short")` - short similar quotes

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• list_memories

  Toolora MCP Server

  Use this tool to discover what has been saved in memory — e.g. at the start of a session, or when the user asks 'what have you saved?' or 'show me my memories'. Returns all saved memory keys with their preview, save date, and expiry. Optionally filter by a prefix (e.g. 'project-' to list only project memories). Pair with recall_memory to fetch the full content of any key.

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• mesh_descriptor

  Medical Terminologies MCP

  Get detailed information about a MeSH descriptor by ID. Use this tool to: - Get the full definition (scope note) of a MeSH term - View tree numbers showing hierarchy location - See related concepts and synonyms Provide a MeSH Descriptor ID like "D015242" (Ofloxacin).

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