Convert Markdown into an editable Microsoft Word (.docx) document. Returns a downloadable URL.
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447,091 tools. Updated 2026-08-12 05:00
"Microsoft Word word processing software" matching MCP tools:
- Generate a word cloud and return it as a WebP image. Data is sent to word-cloud.net servers for processing only and is not logged or stored.Connector
- Generate a word cloud and return an embeddable URL. Data is sent to word-cloud.net servers for processing only and is not logged or stored.Connector
- Generate a word cloud and return it as an SVG image. Data is sent to word-cloud.net servers for processing only and is not logged or stored.Connector
- 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.Connector
- Transcribe audio or video to text, including per-word timestamps for precise editing. Three-call flow: (1) call with `filename` to receive {job_id, payment_challenge}; (2) pay via MPP, then call with `job_id` + `payment_credential` to receive {upload_url} (presigned PUT, 1h expiry); (3) PUT the bytes, then complete_upload(job_id), then poll get_job_status(job_id). On completion, get_job_status returns two outputs: role `transcript` (SRT) and role `transcript-words` (JSON matching /.well-known/weftly-transcript-v2.schema.json, with segment-level and per-word timestamps). For other formats, pass `format=srt|txt|vtt|json|words` to get_job_status to receive content inline — `txt` and `vtt` are derived from SRT, `json` is v1 (segments only), `words` is v2 (segments + words). Flat price: audio $0.50, video $1.00 — see /.well-known/mpp.json for the authoritative table. Use for podcasts, interviews, meetings, lectures, and especially for creating clips, multicamera edits, or edit-video-from-transcript where word boundaries matter. Retrying any call with `job_id` alone returns current state (idempotent). Failed jobs auto-refund.Connector
Matching MCP Servers
- AlicenseBqualityBmaintenanceStructure-preserving Word DOCX editing MCP server with a .NET Open XML backend and Office.js live sessions. Enables safe, auditable, incremental editing of Microsoft Word documents for AI agents.63104AGPL 3.0
- FlicenseBqualityDmaintenanceEnables programmatic generation of Microsoft Word documents (.docx) from AI-generated text and data, with support for Markdown formatting, tables, headers, and rich document elements that are saved directly to the local file system.1
Matching MCP Connectors
Turn a phrase and its translation into a shareable word-alignment diagram.
Generate word clouds from text with custom fonts, colors, backgrounds, gradients, and shape masks
- 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.Connector
- Find trademarks whose WHOLE mark sounds similar to the given mark (Metaphone + trigram, whole-mark similarity threshold). LIMITS: it compares entire marks, so multi-word marks that merely CONTAIN a sound-alike word are invisible to it — "KWIK REWARDS" will NOT surface for a QUICK query even though KWIK sounds like QUICK. Thin or empty results are NEVER evidence that no sound-alike marks exist and NEVER support an availability/clearance conclusion: cross-check with list_marks_containing_term on the likely variant spellings (e.g. KWIK, QUIK, QWIK for QUICK — it enumerates ALL containing marks, including compounds), and answer availability questions with run_knockout_search, the actual clearance engine.Connector
- 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"])Connector
- 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).Connector
- [DEPRECATED — renamed tag_rule_list. Will be removed after 2026-10-07.] List your org's TAGGING RULES (the dashboard's 'Tagging rules') — labels applied to posts your Watchers already ingest. NOT the dashboard's Keyword Monitor: for the keywords that search all of Reddit daily, use keyword_monitor_list. Each rule tags matching Dataset records whose title or body mentions its term as a whole word. Returns the term, active status, and match statistics. Changes take effect on the next scheduled processing cycle. Existing opportunity scores and matches are not retroactively updated. (requires a free Prowlo account — call it to get a signup link)Connector
- List your org's TAGGING RULES (the dashboard's 'Tagging rules') — labels applied to posts your Watchers already ingest. NOT the dashboard's Keyword Monitor: for the keywords that search all of Reddit daily, use keyword_monitor_list. Each rule tags matching Dataset records whose title or body mentions its term as a whole word. Returns the term, active status, and match statistics. Changes take effect on the next scheduled processing cycle. Existing opportunity scores and matches are not retroactively updated. (requires a free Prowlo account — call it to get a signup link)Connector
- 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.Connector
- Show a verse to the user. THE DEFAULT way to display/read a scripture verse: renders an inline card with the original script (centered), transliteration in the requested language, the word-by-word, and the translation — all at once. Use this whenever the user asks to see, read, open, or quote a specific verse ("покажи БГ 2.13", "read Bhagavad-gita 2.13"). The other verse_* tools are for fetching raw data; for DISPLAY prefer this one. Address by ref ("BG 2.13"), source+tokens, or id; lang sets the script + translation language.Connector
- 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.Connector
- 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.Connector
- 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.Connector
- Get synsets (word meanings) for a Danish word, returning a sorted list of lexical concepts. DanNet follows the OntoLex-Lemon model where: - Words (ontolex:LexicalEntry) evoke concepts through senses - Synsets (ontolex:LexicalConcept) represent units of meaning - Multiple words can share the same synset (synonyms) - One word can have multiple synsets (polysemy) This function returns all synsets associated with a word, effectively giving you all the different meanings/senses that word can have. Each synset represents a distinct semantic concept with its own definition and semantic relationships. Common patterns in Danish: - Nouns often have multiple senses (e.g., "kage" = cake/lump) - Verbs distinguish motion vs. state (e.g., "løbe" = run/flow) - Check synset's dns:ontologicalType for semantic classification DDO CONNECTION AND SYNSET LABELS: Synset labels are compositions of DDO-derived sense labels, showing all words that express the same meaning. For example: - "{hund_1§1; køter_§1; vovhund_§1; vovse_§1}" = all words meaning "domestic dog" - "{forlygte_§2; babs_§1; bryst_§2; patte_1§1a}" = all words meaning "female breast" Each individual sense label follows DDO structure: - "hund_1§1" = word "hund", entry 1, definition 1 in DDO (ordnet.dk) - "patte_1§1a" = word "patte", entry 1, definition 1, subdefinition a - The § notation connects directly to DDO's definition numbering system This composition reveals the semantic relationships between Danish words and their shared meanings, all traceable back to authoritative DDO lexicographic data. RETURN BEHAVIOR: This function has two possible return modes depending on search results: 1. MULTIPLE RESULTS: Returns List[SearchResult] with basic information for each synset 2. SINGLE RESULT (redirect): Returns full synset data Dict when DanNet automatically redirects to a single synset. This provides immediate access to all semantic relationships, ontological types, sentiment data, and other rich information without requiring a separate get_synset_info() call. The single-result case is equivalent to calling get_synset_info() on the synset, providing the same comprehensive RDF data structure with all semantic relations. Args: query: The Danish word or phrase to search for language: Language for labels and definitions in results (default: "da" for Danish, "en" for English when available) Note: Only Danish words can be searched regardless of this parameter Returns: MULTIPLE RESULTS: List of SearchResult objects with: - word: The lexical form - synset_id: Unique synset identifier (format: synset-NNNNN) - label: Human-readable synset label (e.g., "{kage_1§1}") - definition: Brief semantic definition (may be truncated with "...") SINGLE RESULT: Dict with complete synset data including: - All RDF properties with namespace prefixes (e.g., wn:hypernym) - dns:ontologicalType → semantic types with @set array - dns:sentiment → parsed sentiment (if present) - synset_id → clean identifier for convenience - All semantic relationships and linguistic properties Examples: # Multiple results case results = get_word_synsets("hund") # Returns list of search result dictionaries for all meanings of "hund" # => [{"word": "hund", "synset_id": "synset-3047", ...}, ...] # Single result case (redirect) result = get_word_synsets("svinkeærinde") # Returns complete synset data for unique word # => {'wn:hypernym': 'dn:synset-11677', 'dns:sentiment': {...}, ...}Connector
- Returns Makuri's pricing plans including what's included in each tier and any usage limits. Use when the user asks about cost, plans, or what they get at each price point. 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.Connector
- Word-overlap based hallucination check: verifies if an LLM answer's words and numbers appear in the provided source/context. Fast, deterministic, no API key needed. Limitations: not semantic — does not understand synonyms or paraphrases. For true semantic grounding, use run_semantic_tests with embedding mode. Essential for quick RAG accuracy testing.Connector