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272,127 tools. Last updated 2026-07-08 06:16

"Understanding the concept of control in Notion or related topics" matching MCP tools:

  • Return a canonical definition for a primitive Eurorack / synthesis concept and its relations to other concepts in the corpus. Use this for VOCABULARY questions, not module questions — when the user is asking what a term means or how two terms relate, not which modules implement it. Typical shapes: - "Is four-quadrant mult the same as through-zero AM?" → lookup_concept("four-quadrant mult") - "What's the difference between a gate and a trigger?" → lookup_concept("gate") - "Modular signal level vs line level — when does it matter?" → lookup_concept("modular signal level") - "Are clock dividers just pulse counters?" → lookup_concept("clock divider") - "Are polyphonic patch cables TRRRRRS?" → lookup_concept("polyphonic cable") Lookup is case-insensitive across three axes, tried in order: the canonical id ("through-zero-fm"), the canonical label ("Through-Zero FM (TZFM)"), and any registered alias ("tzfm", "through zero fm"). Spaces and hyphens are matched literally; the lookup does NOT normalize whitespace beyond lowercasing. If the term doesn't match anything, the response includes up to 5 substring-matched suggestions. Args: - name (string, required, min length 2): the term to look up. Examples: "AM", "ring mod", "four-quadrant mult", "TZFM", "clock divider", "gate", "trigger". Returns: { "concept": { "id": "amplitude-modulation", "label": "Amplitude Modulation (AM)", "description": "A multiplication of two signals: the carrier...", "aliases": ["am", "amplitude modulation", "amplitude mod"], "related_concepts": [ { "related_concept_id": "ring-modulation", "related_concept_label": "Ring Modulation (RM)", "relation_kind": "commonly_confused_with", "note": "AM with a unipolar modulator preserves the carrier..." }, ... ], "source_id": null, "citation_url": "https://learningmodular.com/glossary/...", "citation_quote": "Amplitude modulation is when..." } | null, "_meta": { "query": "<the name argument verbatim>", "matched_via": "id" | "label" | "alias" | "none", "concept_suggestions": [ { "id": "...", "label": "...", "matched_via": "alias", "matched_text": "..." } ], "feedback_hint": "...?" } } Relation kinds: - "related_to" — see-also link (default; symmetric in spirit). - "subtype_of" — X is a specific case of Y (RM ⊂ AM, TZFM ⊂ linear FM). - "inverse_of" — X is the opposite of Y (clock-divider ↔ clock-multiplier). - "commonly_confused_with" — they're distinct, but people conflate them (gate vs trigger, AM vs RM, modular level vs line level). When to cite: every concept carries either source_id or citation_url + citation_quote. Surface the citation when the answer affects a decision (e.g. "the corpus cites learningmodular.com — TRS cables are physically the same connector whether carrying balanced mono or unbalanced stereo; only the destination determines the role"). When the result is null and concept_suggestions are provided, present 2–3 closest matches to the user. If none look right, the corpus genuinely doesn't carry that concept — call report_gap with kind="missing_field" and tool_name="lookup_concept" naming the term and its expected definition.
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  • Create or overwrite an OpenAkashic markdown note. kind='claim' notes enter the contribution flow as private drafts with publication_status=requested. Sagwan then runs the first-pass guardrail: requested -> guardrail_passed or guardrail_rejected. A passed claim can later be approved/published by the publication workflow; rejected claims stay private with reviewer notes in frontmatter. Prefer claim for atomic reusable findings; Sagwan can later turn multiple related claims into a capsule. kind='capsule' notes stay private until you request publication review. Other kinds (playbook, concept, etc.) remain Closed-only working memory. Writable roots: personal_vault/, doc/, assets/ only. Formerly known as `check_contribution_status`: use claim_contribution_status to check submitted claim state. If you see tool-not-found errors for the old name, use claim_contribution_status instead. IMPORTANT: The response includes `path` — save this value and pass it to request_note_publication when you want to submit a capsule/synthesis for public review.
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  • Show the founder an interactive intake form to start their FREE Concept Diagnostic. PREFER calling this over asking for the founder's name, email and concept one message at a time — it collects everything in one card and starts the diagnostic on submit. Call it as soon as the user wants to start, or check the viability of, an idea. The form is deliberately collected FRESH from the founder and starts BLANK — it does NOT accept or pre-populate remembered details, so the founder always enters (and sees) their own name, email and concept. This keeps the destination email accurate (one free diagnostic per founder, emailed to the address they type). Takes no arguments.
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  • 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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  • Run a PromQL query against Control Plane metrics (Prometheus-compatible). Default is a range query over the last hour at 60s step — pass `resolution: "instant"` for a point-in-time query, `since` / `from` / `to` to adjust the window, and `step` to control resolution. Results are sliced to the first 50 series in prose; the full Prometheus response is included as JSON. If you already know the metric, just query it: gauges like `cpu_used`, `mem_used`, `replica_count` are used bare — as are the pre-rated `egress` and `requests_per_second` (never wrap these in rate()); genuine counters need rate(), e.g. `sum by (workload) (rate(container_restarts[5m]))`; latency is a histogram: `histogram_quantile(0.95, sum by (le) (request_duration_ms_bucket))`. Only when you are unsure of the exact metric name or label values — or a query returns no series — call `list_metrics` first to see what is actually present in the org (incl. custom metrics) and a metric’s real labels. Use this to verify autoscaling signals before changing scaling settings — measure first, then change.
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  • 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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  • A Notion workspace is a collaborative environment where teams can organize work, manage projects,…

  • Markdown-first MCP server for Notion API with 8 composite tools and 39 actions.

  • Use this tool when the user asks BOTH what a financial figure is AND which filing reported it — e.g. "What was Apple's most recently reported revenue, and which 10-Q filed it?" or "Show me the accession ID for Tesla's latest net income." Returns a single fact plus its complete filing provenance: entity, concept, period, value, accession ID, filing URL, and form type (10-K, 10-Q, etc.). Use this INSTEAD OF `search_companies` when the user already names a company and wants a financial figure with its source filing — `search_companies` only resolves identifiers and returns no financial data. Use this INSTEAD OF `get_company_fundamentals` when the user explicitly wants the filing/form type or the accession ID — `get_company_fundamentals` returns metrics across periods but omits filing provenance. Two lookup modes: (1) by fact_id (deterministic SHA-256 identity) or (2) by concept name plus a ticker (most recently reported fact). Optionally pin a point-in-time cutoff via as_of_date (YYYY-MM-DD) — returns the latest filing accepted by SEC on or before that date (no look-ahead); check `_meta.pit_safe`. DURATION: a single 10-K tags BOTH a 12-month figure and a 3-month Q4 stub at the same period_end; on a tie this returns the longer (headline) window, and every result carries `period_type` and `period_span_days` so a 3-month stub is never mistaken for the annual figure. Provide either fact_id or concept (required). Returns FACT_NOT_FOUND if no matching fact exists. Available on all plans.
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  • Search the Islam West Africa Collection across newspaper articles, Islamic publications, archival documents, academic references, and the authority index (persons/places/organisations/events/subjects). Pass ONE concept or name — e.g. 'Tijaniyya', 'laïcité', 'Sheikh Gumi', 'pèlerinage'. Matching is accent- and case-insensitive; a multi-word query requires every word to appear somewhere in the item, so prefer a single concept per call. Write query strings and concept keywords in French for press/publication/document/index discovery even when the user's report language is not French. Academic references are multilingual, so try French and English title/abstract terms when relevant; metadata/filter labels remain French. Use the French transliteration of Islamic terms (Tabaski not 'Eid al-Adha', charia not 'sharia', Maouloud not 'Mawlid'). Returns {results:[{id,title,url,category}], ranking}; each result's `category` names its subset and the `ranking` field documents the ordering. Pass an id to `fetch` to read the full text. For filtered queries (by country, date, or newspaper) use the search_* tools instead.
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  • 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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  • SECOND STEP in the troubleshooting workflow. Read the full content and solution of a specific Knowledge Base card. Returns the card content WITH reliability metrics and related cards so you can assess trustworthiness and explore connected issues. WHEN TO USE: - Call this ONLY after obtaining a valid `kb_id` from the `resolve_kb_id` tool. INPUT: - `kb_id`: The exact ID of the card (e.g., 'CROSS_DOCKER_001'). OUTPUT: - Returns reliability metrics followed by the full Markdown content of the card, plus related cards. - You MUST apply the solution provided in the card to resolve the user's issue. - After applying, you MUST call `save_kb_card` with `outcome` parameter to close the feedback loop.
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  • 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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  • Reflect on recent thoughts and patterns. Analyzes recent activity to identify patterns, topics, and insights. Useful for understanding "what have I been thinking about?" By default, only returns user-created memories (not document chunks). Set include_documents=True to also include chunks from uploaded documents. ⚠️ EXPERIMENTAL: - Importance weighting in results not yet implemented. Importance scores are stored but don't affect ranking. Args: time_window: Time period to analyze ('recent', 'today', 'week', 'month', '1d', '7d', '30d', '90d') include_documents: Whether to include document chunks (default: False, only user memories) start_date: Filter memories created on or after this date (ISO 8601: '2025-01-01' or '2025-01-01T00:00:00Z') end_date: Filter memories created on or before this date (ISO 8601: '2025-01-09' or '2025-01-09T23:59:59Z') ctx: MCP context (automatically provided) Returns: Dict with analysis including top memories, active topics, patterns, insights, and any saved contexts (checkpoints) created in the window. Examples: >>> await reflect("recent") {'success': True, 'memories_analyzed': 50, 'active_topics': [...], 'contexts': [...], ...} >>> await reflect("week", include_documents=True) {'success': True, 'memories_analyzed': 150, ...} # includes document chunks >>> await reflect(start_date="2025-01-01", end_date="2025-01-07") {'success': True, 'memories_analyzed': 25, ...} # memories from first week of January
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  • Get historical XBRL financial data for a company. Accepts friendly concept names (e.g., "revenue", "net_income", "assets") or raw XBRL tags. Discover available friendly names with secedgar_search_concepts. Handles historical tag changes and deduplicates data automatically.
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  • Mutating. End your turn and pass control to the opponent. Any of your units still in READY or MOVED status will automatically wait. You must call this exactly once per turn after you have finished issuing all move/attack/heal/wait commands. The opponent's turn begins immediately after. Returns an error if it is not currently your turn.
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  • 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.
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  • 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.
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  • Get Lenny Zeltser's expert CTI writing guidelines. Topics include tone, words, structure, executive_summary, voice, articles, summary, brief (one-page brief section guidance), handoffs (cross-server routing), methodology (the three subsections), fields (per-field guidance), and CTI-specific topics: attribution (full Six Signals prose), confidence (ICD-203 ladder), pyramid_of_pain, six_signals (signals table only), and anti_patterns. The general writing topics (tone/words/structure/executive_summary) now defer to `get_security_writing_guidelines` for the canonical Five Elements rules; CTI-specific content lives in the other topics. Pair the 'fields' topic with field_id for single-field guidance. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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  • Return the complete parent chain for a taxon — from kingdom (or domain) down to the taxon itself — as an ordered array. Each entry has its rank, canonical name, and taxon key. The array is returned root-first (kingdom → phylum → class → … → parent of given taxon). Useful for building taxonomic trees or understanding placement without navigating the backbone level-by-level.
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  • Fetch a work by Open Library Work ID (OL…W). Returns title, description, subjects, cover IDs, and linked author IDs for follow-up lookups. Works represent the abstract book concept independent of any specific edition. Note: author names are not included — use openlibrary_get_author or openlibrary_search_books for names.
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  • 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.
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