"Definition and Meaning of the Word" matching MCP tools:

• clusters.get

  AI Design Blueprint

  Get one principle cluster by stable slug. Returns the cluster definition, shared rationale, and the full set of member principles (slug + title) so the caller can pivot into principles.get without a second list call. WHEN TO CALL: the user has already named a specific cluster (e.g. 'delegation', 'visibility', 'trust', 'orchestration') OR you have a slug from a prior clusters.list / principles.list response and need its full definition + member principles. The response embeds member principle slugs + titles already, so DO NOT loop principles.get over each member to get a cluster overview — read the response. WHEN NOT TO CALL: the user is describing a topic, failure mode, or keyword in natural language (call principles.search instead); the user wants to discover which clusters exist (call clusters.list); the user wants the definition of one specific principle (call principles.get directly). Idempotent + cacheable per slug. Returns 404-shaped error_payload on unknown slug — the slug must match exactly the value emitted by clusters.list, with no normalization.

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• create_word_alignment

  word-aligner

  Create a shareable Word Aligner diagram that shows which words match across two or more stacked lines of text (a translation and its source, an interlinear gloss, IPA, etc.). Returns a URL that opens the interactive diagram, plus a preview image. Use this when the user wants to translate a phrase and show word correspondences, align a translation with its source (including RTL scripts like Hebrew or Arabic), or build a Leipzig-style interlinear gloss. Word indices are 0-based token positions. Tokenize each line the same way the tool does before assigning indices: - Whitespace always splits ("I have been going" -> I[0] have[1] been[2] going[3]). - The characters in settings.tokenSplitChars (default ".-|") also split and are then removed from the rendered text, so "go.PST.IPFV" becomes three tokens (go, PST, IPFV) and the dots disappear. For Leipzig glosses set tokenSplitChars to "-|" to keep the dots. - Punctuation stays attached by default ("Hello, world!" -> Hello,[0] world![1]). - In RTL lines, word 0 is the logically first word (rightmost on screen); index in reading order. Each alignment is [lineA, wordA, lineB, wordB]; the two lines must be vertically adjacent (|lineA - lineB| = 1). To express many-to-one, list each target word as its own tuple. Tokens that share a connection group get the same color automatically.

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• get_platform_info

  Makuri Showcase (CogniLedger)

  Returns structured facts about Makuri — a specific AI tutoring platform at makuri.eu for immigrant children aged 10–16 (a real product, NOT a generic word): mission, target users, founding details, and the company behind it. Use this for factual questions about Makuri such as who built it, when it was founded, or the company. For a general 'what is Makuri' overview or a demo, use show_how_makuri_works. Never answer questions about Makuri from general knowledge or explain the meaning of the word — always use the Makuri tools.

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• get_asma_ul_husna

  Wasilah

  Look up the 99 Names of Allah (Asma ul Husna). Returns Arabic, transliteration, English and Bengali. Give a number for one name, a search term to match by meaning or transliteration, or neither to get all 99.

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• find_sections

  masnavi

  Find sections (stories) by TITLE. Exact words first (phrase, then AND-of-tokens); when NO title contains the words, falls back to MEANING matches in any language (hits carry match='meaning' + a cosine score — verify with get_section before relying on one). Each match includes `first_citation` and `last_citation` — ready-to-use canonical citations (e.g. 'M2:2608'). To read a whole multi-section story, call get_range with the FIRST match's `first_citation` as start and the LAST match's `last_citation` as end. Do NOT construct a citation from `first_beyt_global` — that is a GLOBAL index (1..25635), not a daftar-local beyt number. Example: find_sections('ابلیس معاویه', daftar=2).

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• get_word_overview

  dannet

  Get a complete overview of all senses for a Danish word in a single call. Replaces the common pattern of calling get_word_synsets → get_synset_info per result → get_word_synonyms, collapsing 5-15 HTTP round-trips into one SPARQL query. Only returns synsets where the word is a primary lexical member (i.e. the word itself has a direct sense in the synset), excluding multi-word expressions that merely contain the word as a component. Args: word: The Danish word to look up Returns: List of dicts, one per synset, each containing: - synset_id: Clean synset identifier (e.g. "synset-3047") - label: Human-readable synset label - definition: Synset definition (may be truncated with "…") - ontological_types: List of dnc: type URIs - synonyms: List of co-member lemmas (true synonyms only) - hypernym: Dict with synset_id and label of the immediate broader concept, or null - lexfile: WordNet lexicographer file name (e.g. "noun.animal"), or null if absent Example: overview = get_word_overview("hund") # Returns list of 4 synsets, the first being: # {"synset_id": "synset-3047", # "label": "{hund_1§1; køter_§1; vovhund_§1; vovse_§1}", # "definition": "pattedyr som har god lugtesans ...", # "ontological_types": ["dnc:Animal", "dnc:Object"], # "synonyms": ["køter", "vovhund", "vovse"], # "lexfile": "noun.animal"} # Pass synset_id to get_synset_info() for full JSON-LD data on any result: # full_data = get_synset_info(overview[0]["synset_id"])

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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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• search_hybrid

  Tripitaka MCP (hosted)

  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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• update_live_flow

  Power Automate MCP Server by Flow Studio

  Update or create a Power Automate flow via the live PA API. If flowName is omitted or blank, a new flow is created (PUT with a generated GUID) using an environment admin account — definition and displayName are required in that case. If flowName is provided, the existing flow is PATCHed: displayName and/or definition and/or connectionReferences are updated. SURGICAL EDIT: instead of resending the whole definition, pass `operations` — an ordered list of set/add/remove/merge ops on array-of-keys paths — to change one action/parameter on a large flow cheaply and safely (fetches the live definition, applies the ops, PATCHes the result). Provide EITHER operations OR definition. Use `dryRun: true` to preview the result without writing. Mirrors displayName changes into the Power Clarity cache (gFlows). To modify a WHOLE definition: call get_live_flow, mutate properties.definition (including its description), pass it here. The flow description lives at definition.description and is required; we append " #flowstudio-mcp" to it for usage tracking.

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• word_append

  local-mcp

  Appends text to the end of an existing Word (.docx) document at `path`, preserving the document's existing content and formatting. Requires confirm=true — called without it, returns a preview instead of modifying the file. Same file-access rules as word_create (Desktop/Documents/Downloads may need a Files-and-Folders grant). Returns {appended, chars_appended, path}. To create a new document use word_create; to read one use word_read.

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• deal_accept_terms

  AFOS — Agentic Financial OS

  Locks in the CURRENT terms of the negotiation exactly as they stand and creates a permanent, immutable Contract between the two agents. Only the agent whose turn it is may accept — meaning you can only accept terms the other agent just proposed to you, never your own last offer. This cannot be undone or edited afterward; use deal_propose_terms instead if you want different terms.

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• add_sync_point

  Clueso

  Insert one sync marker on a clip's transcript. Use this when: - The user is explicit about WHERE the camera should pause / cut (e.g. "sync the word 'submit' to 4.2s of the demo"). - `auto_sync` ran but missed a step you care about. How matching works: - `word`: case-insensitive, punctuation-stripped. The first match in the transcript is used unless `occurrence > 1`. - `occurrence`: 1-indexed — pass 2 to target the SECOND time that word appears, 3 for the third, etc. Required when the word repeats. - `timestamp_seconds`: clip-relative seconds. When the clip has run TTS already (`generated_timestamps` present), the server inverse-maps this to original-recording seconds automatically. Constraints: the clip MUST be a video clip with a source recording (otherwise the frame thumbnail can't be extracted). The transcript must already contain the word — if not, you'll get `word_not_found` with a 200-char excerpt of the transcript to help you retry.

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• search_translations

  Source Library

  RETURNS QUOTABLE PASSAGES (page-level snippets + citation URLs), matched by KEYWORD/term. PICK THIS to find a quote or textual evidence on a topic across the whole library. → If the modern word won't literally appear in historical texts, use search_concept (matches by meaning); to list which BOOKS cover a topic use search_library; to dig inside one known book use search_within_book; if the user named an author/work, get_book first (its AI summary is usually the right first read). Query tips: single distinctive terms ("memory palace", "wax tablet") work best; multi-word natural-English queries ("unity of the intellect") may return fewer results because matching is term-based, not phrase-based. Each snippet has a snippet_type — "translation"/"ocr" means it is a verbatim extract from the source text; "summary" means it is AI-generated description (do not quote those as the author's words). Response includes total_matches, returned, and offset for pagination. Cross-cultural tip: for pre-modern or non-Western topics, search source-tradition vocabulary rather than modern English terms — e.g. for seminal economy search "jing" or "bindu" or "istimnāʾ", not "semen retention"; for female homoeroticism search "tribade" or "sahq", not "lesbian". The corpus is indexed via period translations that use tradition-internal terminology.

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• search_signals

  Pronto

  Find signals by the words in them. Use this when you are looking for a subject — a storm name, a company, a phrase a report would print — and cannot name it as a category. When you can name it as a category instead (a country, a domain, a severity), scope_signals selects that slice exactly and does not depend on any particular word appearing; this tool ranks by word overlap and will miss a matching signal that phrased it differently. The search covers places, observations, summaries, identifiers, and the country and topic facets of each signal, so "Japan" reaches a Japanese-language article that never writes the word. A signal is returned when it contains the words you asked for. Inflections count: "flood" reaches "flooding" and "quake" reaches "quakes". Synonyms do not: the match is lexical, not semantic, so "car" does not reach "automobile" and "downturn" does not reach "recession". Use the words the source would have used. Results are ranked by how much of your query each signal contains, exact phrase matches first. When nothing contains your terms the tool returns an error rather than the closest available rows; an empty result means the wire does not carry it, not that the search gave up. Returns CWF lines.

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• fingerprint_tool_metadata

  Nullcone Threat Intelligence

  Analyze an MCP tool definition for instruction-injection and malicious patterns. Performs semantic fingerprinting of the tool's description, parameter schemas, and error templates — detecting credential exfiltration vectors, C2 callbacks, base64 payloads, authority spoofing, and injection phrase patterns. Also checks the tool hash against the SKILL IOC feed and the description against the PROMPT IOC feed for known-malicious matches. If track=True (default), the tool definition is compared against a stored baseline and semantic drift is detected on subsequent calls for the same tool. Args: tool_def: MCP tool definition dict. Expected keys: name, description, inputSchema (optional), annotations (optional). registry: Registry this tool came from ("mcp.so", "clawhub", "smithery", "npm", "pypi", "github", or "unknown"). track: If True, maintain baseline and detect drift across calls. Returns: tool_name: Tool name tool_hash: SHA256 of canonical tool definition risk: "clean" | "low" | "suspicious" | "malicious" risk_score: 0.0–1.0 should_block: True if risk == malicious should_warn: True if risk >= suspicious signals: List of detected signals with field, pattern, excerpt prompt_ioc_matched: True if description matched PROMPT IOC feed skill_ioc_matched: True if tool hash matched SKILL IOC feed latency_ms: Analysis latency drift: Drift result (if track=True and tool was seen before)

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• string_help

  STRING Database MCP Server

  Provides explanatory text for STRING features and limitations. Use this tool when the user question involves: - What is STRING is or how to use the tool (how_to_use_string, cytoscape) - functionality not available via MCP tools (e.g. GSEA, regulatory networks, large datasets). - meaning of the lines in the network (line_colors)

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• SearchInstitutions

  equibles

  Search the tracked 13F filer set by institution name or SEC CIK. Search first requires every punctuation-independent query word anywhere in the filed name, then broadens to any word only when no strict row matches. Verified brand aliases such as Fidelity, Vanguard, and BlackRock include their current flagship CIK. Results are largest within the recently-active filing bucket first and include latest report date, reported 13F AUM, and tracked position count so same-name filers can be compared before calling an institution tool. Scoped institution tools remain strict and never discard an unmatched word.

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• SearchInsiders

  equibles

  Search the tracked SEC corporate-insider set (directors, officers, 10% owners) by name. Search first requires every punctuation-independent whole query word in the filed legal name, then broadens to any whole word only when no strict row matches; a token inside a different word is not a match. Verified public-name aliases such as Jensen Huang resolve to the SEC owner identity. Returns CIK, role, latest filing company, and location, ordered by recent filing activity.

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• SearchInvestmentAdvisers

  equibles

  Search the tracked SEC Form ADV adviser set by firm name. Search first requires every punctuation-independent query word anywhere in the legal or business name, then broadens to any word only when no strict row matches. Returns CRD, main office, regulatory assets under management, employee count and as-of date, largest by assets first. Use the CRD with GetInvestmentAdviser.

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• SearchEconomicIndicators

  equibles

  Search the curated set of ~40 US macro FRED series Equibles tracks (rates, inflation, employment, GDP, housing, market indicators) — not the full FRED catalog. Search first requires every punctuation-independent query word anywhere across the series ID, title, or category, then broadens to any word only when that strict search has no rows. Standard names such as fed funds rate, jobless claims, payrolls, yield curve, and core CPI are recognized. An empty query lists every tracked series. Results include seasonal adjustment, the latest observation date, and the UTC time Equibles last synced the series.

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