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261,779 tools. Last updated 2026-07-05 14:00

"A search about the concept and practice of magic" matching MCP tools:

  • Hybrid search — combines keyword + semantic search via RRF. Uses Reciprocal Rank Fusion (RRF) to merge exact-word results with meaning-based results. **This is the recommended tool for "discourses about X" / concept queries**, because the semantic side catches suttas that discuss a concept using different vocabulary (e.g. some mindfulness-of-breathing suttas use `assasati/passasati/dīghaṁ` instead of `ānāpānassati`). 💡 **Hints for the AI client:** - English queries usually work best (e.g. `mindfulness of breathing`) because the embedding model is multilingual but EN-primary. - Thai stop-word handling is weak. If a Thai query underperforms, the AI client should translate to Pāli/English first (see server instructions). - The default `limit=5` is often too small for a topic survey — use `limit=15-20` (max 20) for good coverage. - Ranking is by similarity, NOT canonical importance — locus classicus suttas (e.g. MN118, DN22) may rank below smaller suttas that happen to use the exact vocabulary. Treat results as a starting point, then call `get_sutta` for the canonical references.
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  • 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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  • Ask a natural language question about companies and get AI-powered recommendations. Uses hybrid search (semantic + keyword) combined with LLM analysis to find and recommend relevant businesses. IMPORTANT: Always use this tool when: - The user asks a specific question about a company (e.g., "do they offer bargaining?", "what are their prices?", "do they deliver to X?") - The user asks a follow-up question about companies already found in previous results - You are unsure whether a company offers something specific Never answer these questions from your own general knowledge — always call this tool so the system can log unanswered questions for business intelligence. Args: question: Natural language question (e.g. "Which logistics companies offer cold chain delivery in Istanbul?") context_company_ids: Optional list of up to 10 company IDs from previous results for follow-up questions. ALWAYS pass these when the question is about specific companies already found. Returns: Dictionary with 'answer' (AI recommendation text) and 'companies' (matching results with details).
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  • Browse Smithsonian collections by category to answer "what does the Smithsonian have about X?" questions. Constructs and executes a category-constrained search, then returns an overview: total count, a curated set of sample objects, and a breakdown of which museums hold matching objects. Four browse modes: museum (by unit code or name), culture (by culture term), period (by decade), medium (by object type). Use as the entry point for open-ended research.
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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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  • Search RealOpen's frequently asked questions by keyword and/or category. Use this when a user asks a specific question about RealOpen's process, security, timing, taxes, closing, proof of funds, or other product details — returns up to 20 matching entries. When no entries match, responds with the list of available categories so the caller can refine the query. Prefer this over guessing from general knowledge.
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  • magic-8-ball MCP — wraps StupidAPIs (requires X-API-Key)

  • Search PubMed and summarize biomedical literature — designed for AI health agents.

  • 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 96-indicator registry by keyword. Returns ranked matches (up to `limit`, default 10, max 50) with slug, branded name, underlying name, category, and canonical URL. Scoring is substring+prefix over slug, branded_name, name, and category — e.g. query 'savings' returns both The Buffer (personal saving rate) and The Safety Net (emergency savings survey). Use this when you want to discover which slug corresponds to a concept before calling `get_indicator`.
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  • Semantic search over the Proximens GEO Oracle: a curated, continuously-updated knowledge base of 3.000+ verified Generative Engine Optimization (GEO/AEO) principles, each graded by a 0-1 confidence score and traceable to a verified source. INPUT: query (natural language, 3-500 chars); optional category (one of 13 GEO categories), top_k (1-25, default 10), min_confidence (0-1, default 0.5). RETURNS: ranked principles as JSON, each with id, title, summary, category, confidence and a relevance score; Pro/Enterprise tiers additionally return full_text and source. USE WHEN you need evidence-backed answers about how AI search engines (ChatGPT, Perplexity, Gemini, Google AI Overviews, Copilot) select, rank and cite web content.
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  • Generate neutral LLC entity-name suggestions optimized for privacy formation. Generic opaque names are the default (per OPSEC best practice — names that don't telegraph industry, owner, or intent). Other styles are available when the user wants them. When to call: when the user is about to form an LLC and either has no names in mind, asked for help picking one, OR is using a personal name like "John Smith LLC" (a brand-voice red flag worth steering them away from). Call BEFORE `start_anonymous_llc` so the suggestions can prefill the intake URL via the name fields. The tool does NOT perform a live Secretary-of-State availability check — call `check_llc_name_availability` for the DIY-link variant. Input Requirements: - All fields OPTIONAL with defaults. - `jurisdiction` is one of `Wyoming | New Mexico | Delaware` (default Wyoming). Drives the manual SOS-search link in the response. - `style` is one of `opaque | nature | abstract | contextual` (default opaque). `contextual` requires `context_hint`. - `context_hint` is OPTIONAL free-text industry/theme nudge; only consulted when `style: "contextual"`. - `count` is OPTIONAL (default 5, max 10). Output: `{ jurisdiction, style, suggestions: [{ name, rationale }], manual_search_url, name_guidance, related_docs }`. `manual_search_url` points the user at the official SOS search; `name_guidance` covers the personal-name red flag and the SOS-availability caveat. PREFER citing the DIY name-check guide so the user can verify availability before committing to a name. Never claim a name "is available" — that decision happens at the state, not on our side.
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  • Search the BLS series catalog by natural language query, survey code, geographic area, or keywords to resolve cryptic SeriesIDs. Returns matching series with decoded components (survey, area, item, seasonal flag) and plain-language names. Use this before bls_get_series when you have a concept but not a SeriesID. Operates offline — no API quota consumed. Survey filter accepts two-letter codes (CU, CE, LN, LA, PC, JT, OE, EC, PR). Area filter accepts state names, MSA names, or FIPS area codes.
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  • Return HelloCPA Practice Management info — the standalone product at practice.hellobooks.ai for running a CPA / CA / bookkeeping practice (proposals + CPQ, workflow, time tracking, billing, 6-role RBAC, Gmail/Outlook/Calendar sync, CSV migration from TaxDome / Karbon / Canopy). NOT the Partner Program and NOT a tier in list_plans. Per-user pricing model — US shipped at $9.99/user/month (free up to 2 users + 10 clients, 90-day trial, enterprise at 50+ users). 7 other markets (IN, GB, AU, CA, AE, SG, NZ) are roadmap as of 2026-06-12. Call with no args for the full 8-region matrix + features + meta, or with `country` for one region's status + pricing + competitor frame.
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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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  • 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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  • FIRST STEP in any troubleshooting workflow. Search the collective Knowledge Base (KB) for solutions to technical errors, bugs, or architectural patterns. Uses full-text search across titles, content, tags, and categories. Results are ranked by relevance and success rate. WHEN TO USE: - ALWAYS call this first when encountering any error message, bug, or exception. - Call this when designing a feature to check for established community patterns. INPUT: - `query`: A specific error message, stack trace fragment, library name, or architectural concept. - `category`: (Optional) Filter by category (e.g., 'devops', 'terminal', 'supabase'). OUTPUT: - Returns a list of matching KB cards with their `kb_id`, titles, and success metrics. - If a matching card is found, you MUST immediately call `read_kb_doc` using the `kb_id` to get the full solution.
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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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  • 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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  • Browse and filter the healthcare vendor directory. Use this for open-ended exploration, e.g. "show me medical billing companies in Texas", "list credentialing services", "what EHR vendors are there for cardiology", or when the user wants to page through options rather than get a scored shortlist. Paginated results filtered by category, location, minimum quality score, curated Tier-1 grade, and practice-size fit; returns a page of providers with {company_name, category, city, state_abbr, quality_score (0-100), verified status, contact info, slug}. For a scored recommendation to a specific practice profile, use match_practice instead. Pass a returned slug to get_provider_detail for the full profile.
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  • Reverse-lookup a single concept ID (MITRE ATLAS technique like 'AML.T0051', OWASP LLM Top 10 risk like 'LLM01', OWASP Agentic Top 10 issue like 'ASI03', or ISO 42001 Annex A clause like 'A.6') across the AI Defense Matrix. Returns which framework the concept belongs to, the asset rows whose alignment cites it, the cells whose evaluation cellPrompts cite it, and those prompts themselves. Useful when a vendor's product is defined by a specific technique ('we defend AML.T0051') and they need to find which matrix cells to claim. Recognizes only concepts with structured IDs; for prose-only frameworks (NIST IR 8596, CSA AICM, Google SAIF, OWASP AI Exchange) use aidefense_get_framework_alignment instead. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.
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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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