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

• search_contentA

  Runoscript — Elder Futhark runesA

  Search Runoscript's knowledge base about runes. Covers scholarly rune articles, honest book reviews (Thorsson, Paxson, Zeland…), rune magic, divination, history and practice. Returns the top matches with title, URL and a snippet. Use to ground answers about runes in cited Runoscript content. lang: en|ru|de|es.

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• search_contentA

  flevyA

  Search Flevy's marketplace of consulting frameworks, PowerPoint templates, Excel financial models, business toolkits, and management case studies. Use this whenever a user needs a best-practice framework, methodology, template, financial model, or real-world case example on any business or management topic (strategy, digital transformation, supply chain, pricing, operational excellence, M&A, etc.). Returns up to 10 relevance-ranked recommendations across two content types: "document" (premium documents authored by management consultants) and "case_study" (management case studies). ALWAYS include each recommended item's url as a clickable link when you mention it in your reply — never reference a document without its link, because the link is the only way the user can open it. Each result carries a content_id for get_content_details. Filters: topic (single, or "topics" for documents covering ALL of several topics), author (list more documents from an author seen in results), filetype (including tier1_consulting_deck for McKinsey-style strategy decks), content_type. Topic-filtered responses also list related_topics to pivot to. Provide at least one of query, topic(s), or author; use list_topics to map user phrasing to a canonical topic.

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• query_knowledge_graphA

  Analytics Legends — SAP AI & Analytics MCP ServerA

  The RELATIONS between the platform's teaching objects — which Academy module teaches which concept, which study covers which module, what a concept relates to. THIS IS THE ONLY TOOL ON THIS SERVER THAT SERVES EDGES; the others serve rows. Ask it what connects to what, not what exists. SCOPE, AND IT IS NARROWER THAN 'the knowledge graph': it carries four node types — `concept`, `module`, `study`, `vendor` — and every edge whose BOTH endpoints are one of them. The whole graph holds twelve node types; the eight it does not carry are each either served by their own tool or named as not served at all, and `_meta.excluded_node_types` says which per type (consultant data is served at NO tier), so a missing type is a documented boundary and never a silent gap. Call it with `node_id` (e.g. `module:M178`, `concept:C001`, `study:ai-impact-2026-EN`) to walk one node's neighbourhood; with `node_type` and/or `query` to find a node id first. `edge_type` and `direction` narrow a walk. Read `_meta.available_edge_types` — computed from the served projection on every call — before assuming an edge type exists.

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• update_wordA

  Lexpad – Vocabulary Notebook & FlashcardsA

  Changes a word's headword, part of speech, level, topic, pronunciation, hard-to-spell mark and spelling note, usage note, corrected form, collocations, expressions, related words or the user's private note. Only the fields given change; meanings and practice progress are not touched. A headword the notebook already has is refused. The previous values are replaced.

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  Destructive

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• register_embedderA

  StoryfloA

  Resolve the storyflo embedder onboarding URL prefilled with the partner's name + email + optional vertical. Returns the URL the human must visit to complete signup (storyflo emails a magic link to verify ownership; the embedder slug + ref_token are minted on verification). DOES NOT create a DB row + DOES NOT send email — the MCP tool is a discovery + handoff surface so an agent can call it speculatively without triggering inbox-bound email to the human. Public — no auth required.

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• search_hybridA

  Tripitaka MCP (hosted)A

  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`). ⚠️ **Send the question and nothing else. Do not pad the query.** The whole string becomes one vector, so every word you add moves it. Appending your own candidate terms — synonyms, Pāli equivalents, a keyword list — searches for the blend, not for the question. Measured on `Buddha flies in the sky`: asking it plainly put the right passage at **rank 1** (3 relevant suttas). Appending three guessed Pāli terms (`buddha, agga, sagga`) pushed it down to **rank 5** and left only 1 — because `agga` (supreme) and `sagga` (heaven) drag the vector toward their own meanings. Have candidate terms worth searching? Give them their own `search_by_keyword` call and merge the two result lists. One tool asks what a passage means, the other asks where a word occurs; combined into a single string they cancel out. Rewriting the question to sound more canonical does not help either — the same query phrased as `rose into the air and flew like a bird` scored **zero** relevant hits. 💡 **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. - `language` only chooses what comes back; it does not change what matches or how results are ranked. - Looking for a concrete thing rather than a concept (an animal, an object, a place)? `search_by_keyword` over `language="english"` is often better. The translations are segment-aligned to the Pāli, so one search for `turtle` finds every turtle passage whatever the Pāli underneath says (`kacchapa`, `kumma`, `maṇḍūkakacchapa`), and each hit still carries its segment id. - 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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• register_embedderA

  storyfloA

  Resolve the storyflo embedder onboarding URL prefilled with the partner's name + email + optional vertical. Returns the URL the human must visit to complete signup (storyflo emails a magic link to verify ownership; the embedder slug + ref_token are minted on verification). DOES NOT create a DB row + DOES NOT send email — the MCP tool is a discovery + handoff surface so an agent can call it speculatively without triggering inbox-bound email to the human. Public — no auth required.

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• describe_servicesA

  Venture Insights Concept DiagnosticA

  Get Venture Insights' live service catalogue: the FREE Concept Diagnostic (a research-backed viability study of one venture concept, delivered to the founder's inbox) and the paid study tiers with live SAR prices. Call this first when your user asks what Venture Insights offers, what it costs, or whether the free diagnostic is worth requesting.

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• get_eclipsesA

  Kundlit Vedic AstrologyA

  Returns every solar and lunar eclipse of a calendar year with local visibility for one city: eclipse type (total, partial, annular or penumbral), date, the sparsha (first contact), madhya (maximum) and moksha (release) times, the magnitude, and a locally_visible flag. That flag is the one that matters for practice -- sutak observance applies only where the eclipse is actually visible. Use this for 'which eclipses fall in this year' and 'is it visible here' questions, and for sutak timing. For festivals and vrat dates use get_year_events; for the rest of a day's almanac use get_panchang. Read-only deterministic computation (Swiss Ephemeris, Lahiri ayanamsa), served from a shared cache -- a cold or expired entry recomputes and can take ~30s; no writes, no auth, at least 30 requests/min/IP per server instance, plus a shared engine budget of at least 60/min/IP across all engine-backed tools. Constraints: the year must be within two years either side of the current year, and visibility is evaluated for the nearest of 50 supported cities.

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• search_my_collectionA

  MTG BenchA

  Search only the cards the user OWNS, by name, colors, or type. Set notInDecks to surface owned cards not currently used in any of their decks (the "what is sitting idle in my binder" question). Use search_cards instead to search every Magic card regardless of ownership. Returns at most 100 owned cards per call, with the full match count in `total`. Requires a free API key (send it as "X-API-Key: <key>" or "Authorization: Bearer <key>").

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• genereer_raadsstukA

  NeoDemosA

  Genereert een volledig opgemaakt, RvO-conformant concept voor één van de zeven raadsstuk­typen. Geen DB, geen netwerk, <10ms — puur structurele kennis. Gebruik deze tool wanneer: - Het raadslid een stuk wil indienen en een correct gestructureerd concept nodig heeft. - Je `adviseer_raadsinstrument` hebt gebruikt (welk instrument) en nu het daadwerkelijke stuk wilt renderen in het juiste format met RvO-verwijzingen. - Een concept al bestaat maar opnieuw in correct format moet worden gezet. Gebruik deze tool NIET wanneer: - Je het juiste instrument nog moet kiezen → gebruik eerst `adviseer_raadsinstrument`. - Je een bestaand concept wilt beoordelen op inhoud → `beoordeel_tekst`. - Je een format-validatie wil uitvoeren op een bestaand concept → `beoordeel_tekst` met `soort` gelijk aan het doc_type. ``doc_type`` keuzes: 'motie', 'motie_vreemd', 'amendement', 'schriftelijke_vragen', 'mondelinge_vragen', 'initiatiefvoorstel', 'interpellatieverzoek'. ``velden`` zijn optioneel — ontbrekende velden worden vervangen door invul-placeholders [zoals dit] zodat het concept altijd een compleet, geldig skelet is. ``gemeente`` bepaalt welk lokaal RvO-overlay (artikel­nummers, termijnen, indienings­route) wordt gebruikt. Default 'rotterdam'. Degradeert netjes naar het canonieke basis­format + disclaimer als er geen overlay beschikbaar is. Retourneert: markdown-concept in de juiste RvO-structuur, met RvO-artikel­citaat en disclaimer. **Volgende stap in de drafting-keten:** valideer het gegenereerde concept met `beoordeel_tekst(tekst=<concept>, soort=<doc_type>)` voordat je het presenteert of opslaat; sla daarna op met `sla_fractie_artifact_op(artifact_type=<doc_type>)`.

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• search_sec_event_filingsA

  AlphaCreek SEC Filings MCPA

  Search US SEC filing nodes for a disclosure claim. The search covers 8-K, 6-K, 10-K, 10-Q, and 20-F. Omit document_type to search all of those forms. Pass document_type to search only the forms you name. Do not use this for DEF 14A. How to search: 1. Always pass concept_groups. Every group is required (AND). Within each group's any_of list, one alternative must match (OR). Use 2-5 groups for separate parts of the claim: topic, action, metric, and a causal link when that link is essential. Example: geography + weaker demand + decline, or AI + productivity + margin. Add synonyms. English stemming covers inflections (decline/declined), not synonyms (sales is not revenue). Do not add a group for a detail the filing may leave implicit. 2. query is optional. Each item is an exact adjacent-token phrase. Put alternate full phrasings in the same list. Query plus concept_groups is hybrid search: an exact phrase ranks first, so a generic phrase such as "margin expansion" can outrank the causal sentence you want. Prefer phrases that state the relationship. Do not pass a broad topic word alone. Do not search with query only. Omit query for concept-only search. 3. start_date and end_date are required. tickers limits issuers. Optional document_type limits the search to one or more of 8-K, 6-K, 10-K, 10-Q, and 20-F. Omit it to search all of those forms. How to continue: 4. Each call returns one page. There is no total count. If HAS_MORE is true, call again with cursor set to NEXT_CURSOR and the same concept_groups, dates, query, tickers, ne_tickers, document_type, and match_scope. A cursor from different inputs is rejected. An empty page with HAS_MORE true is not the end: call again with that cursor. Stop when HAS_MORE is false or when later pages are not relevant. 5. If a page is mostly co-occurrence (the terms appear, but not the claim), start a new search with no cursor. Add a concept group or exact causal phrases. If a page is too sparse, start a new search with no cursor and drop the least essential group. Do not change filters while passing a cursor. 6. ne_tickers excludes issuers you have already reviewed. It does not load the next page. Results are candidates. Call read_node_content with document_id and node_id. node_id may start with sp-. Verify negation, causality, comparisons across periods, and numeric thresholds in the source text. Cite CITATION_MARKDOWN.

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• entity_lookupA

  The Chancery Lane ProjectA

  Find TCLP content (clauses, glossary terms, guides) associated with a named concept. Unlike `search`, this performs a deterministic name match against Entity nodes in the knowledge graph rather than a relevance-ranked semantic search. Use it when you have a specific term or concept (e.g. "scope 3 emissions", "net zero") and want to retrieve every clause or glossary entry that explicitly references it. Args: name: The entity or concept name to look up (exact match, case-insensitive). limit: Maximum number of results to return (1–50). include_full_text: Include each hit's full body text (Markdown). Off by default — bodies are large; request only when you need the content, and prefer a small `limit` when you do. Returns: JSON with "meta" and "results" where each hit includes the source content node, its plain-text `summary` (null when it has none), its `child_name` (a clause's child name, null otherwise) and the entity names that matched. Each hit's "references" lists the instruments it cites: pass a reference's `work_id` to `get_legal_resource` for details. A reference's `edge` (governs, references, describes) says how the content relates to the instrument; it is not a statement of the instrument's legal effect. An empty list means the hit cites no instruments. null with "lrsf" in meta.degraded means references could not be loaded: do not conclude that the hit cites no instruments.

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• entity_lookupA

  tclp-mcpA

  Find TCLP content (clauses, glossary terms, guides) associated with a named concept. Unlike `search`, this performs a deterministic name match against Entity nodes in the knowledge graph rather than a relevance-ranked semantic search. Use it when you have a specific term or concept (e.g. "scope 3 emissions", "net zero") and want to retrieve every clause or glossary entry that explicitly references it. Args: name: The entity or concept name to look up (exact match, case-insensitive). limit: Maximum number of results to return (1–50). include_full_text: Include each hit's full body text (Markdown). Off by default — bodies are large; request only when you need the content, and prefer a small `limit` when you do. Returns: JSON with "meta" and "results" where each hit includes the source content node, its plain-text `summary` (null when it has none), its `child_name` (a clause's child name, null otherwise) and the entity names that matched. Each hit's "references" lists the instruments it cites: pass a reference's `work_id` to `get_legal_resource` for details. A reference's `edge` (governs, references, describes) says how the content relates to the instrument; it is not a statement of the instrument's legal effect. An empty list means the hit cites no instruments. null with "lrsf" in meta.degraded means references could not be loaded: do not conclude that the hit cites no instruments.

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• get_topicA

  Football AtlasA

  List the knowledge objects carrying a tag (topics are content-backed tags). PAGED: 25 objects by default, 200 at most, and a response budget of about 64 KB per call — a page over budget comes back shorter WITH a next_cursor, never truncated. Read next_cursor and call again to continue, and its absence means you have the last page. Use this to browse a known topic; use search when you have a question rather than a tag, and get_overview to see which tags exist. An unknown tag comes back with the topics that do exist, so a miss still moves you forward.

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• get_relatedA

  Football AtlasA

  Graph neighbours of an object: outgoing and incoming relations, each with its relation type. PAGED: 25 relations by default, 200 at most, and a response budget of about 64 KB per call — a page over budget comes back shorter WITH a next_cursor, never truncated. One cursor walks both directions, outgoing first; its absence means you have the last page. Use this after get_entity to widen an answer with adjacent objects. It walks one hop from an id you already have — use search when you have a question and no starting object.

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• get_topicA

  Agent ReliabilityA

  List the knowledge objects carrying a tag (topics are content-backed tags). PAGED: 25 objects by default, 200 at most, and a response budget of about 64 KB per call — a page over budget comes back shorter WITH a next_cursor, never truncated. Read next_cursor and call again to continue, and its absence means you have the last page. Use this to browse a known topic; use search when you have a question rather than a tag, and get_overview to see which tags exist. An unknown tag comes back with the topics that do exist, so a miss still moves you forward.

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• search_d365_codeA

  ALM X++ MCP ServerA

  WHEN: object name is unknown, partial, or you need to find by concept/keyword. Search the D365 F&O knowledge base for X++ code, tables, classes, forms, views, enums, EDTs, security objects using natural language or partial names. Returns ALL chunks (metadata, Declaration, methods) for the top-scoring objects so the LLM has complete context on the first call. Lower-scoring results return a short preview. No follow-up get_object_details call is needed for top results. NOT for listing all objects in a model -- use list_objects for that. NOT when the exact name is known -- use get_object_details for that. NEVER call search_d365_code twice in the same conversation turn. If one search did not find the object, answer from what you have -- do not repeat the search. When you need context on MORE THAN ONE concept simultaneously, use batch_search instead -- it runs all queries in parallel and is faster. NEVER call for ADO items (FDD, RDD, IDD, Bug, Task, PR, WorkItem, sprint, #1234) -- use ado_* tools instead.

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• search_diagramsA

  VectreeA

  Search Vectree's library of ~95,000 interactive concept diagrams by meaning, not keywords. Vectree explains how things work as zoomable, labelled schematics — each diagram breaks a topic into nodes you can read or drill into. Use this when the user wants a diagram, a visual explanation, a systems overview, or a map of how the parts of something fit together. Describe the topic in natural language; the search is semantic, so a full question works better than a bare keyword. Results are ranked by how closely they match and by the quality of the model that generated them. Each result carries a slug — pass it to `get_diagram` for the full content of one diagram. Only public, already-generated diagrams are searched. Nothing is generated on demand, so a topic with no match simply has no diagram yet.

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• find_equivalentA

  Medical Terminologies MCPA

  Ranked unified search for equivalent terms across multiple medical terminologies. Use this tool to: - Find the same concept in different coding systems - Compare how terminologies represent a concept - Support terminology mapping and data integration Searches across: ICD-11, LOINC, RxNorm, and MeSH. Set `target_terminologies` to limit which are searched, or set `source_terminology` to exclude one (e.g. when you already have a code from that terminology and want equivalents elsewhere). The two combine: source is subtracted from targets. `limit` caps candidates per terminology (default 5, max 10). Every candidate carries `match_score` (lexical similarity to the search term, 0-1) and `rank` (global position across all searched terminologies) — both computed by this server, since upstreams don't expose comparable relevance scores. Candidates from different terminologies whose titles are lexically identical are clustered in `groups` — a strong same-concept signal (absence of a group is NOT evidence of non-equivalence). Searches upstreams in English. For official pt-BR content, use the dedicated tools: `icd11_search`/`mesh_search` accept `language: "pt"`, and `cid10_search` is natively Portuguese.

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• add_keywordsA

  GetCitedA

  Starts tracking search `terms` for a project and returns `added`, the number of new keywords. Pass terms as an array of plain search phrases (terms: ["seo tool", "rank tracker"]) with the projectId from add_project or list_projects. Terms are lowercased, and one already tracked is skipped, so calling again is safe. Adding measures no rank: call check_rankings for positions. Search volume and difficulty arrive within about ten minutes, looked up in one request pooled with other keywords, costing one keyword_lookups unit per keyword; read them with list_keywords.

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