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309,755 tools. Last updated 2026-07-28 07:48

"Generate Word documents" matching MCP tools:

  • docraptor_create_document

    docraptor

    Generate a PDF or Excel document from HTML (document_content) or a URL (document_url). Exactly one of document_content / document_url is required. By default the document is HOSTED and the tool returns a { download_url } you can fetch — ideal for agents (no large binary in the response). Set hosted:false to get the raw document back as base64, or async:true to enqueue a job and poll docraptor_get_document_status. IMPORTANT: real documents consume account credits (billed). Set test:true to generate a FREE, watermarked document while developing. DocRaptor API: POST /docs.
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  • word_frequency

    Marketing Mcp

    Count word frequencies in text for content/marketing analysis. Returns: {words: [{word, count, pct}], total}
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  • mesh_inbox

    concordance

    The messages that reached you, each carrying its own offline verification so you trust it by proof rather than by this server's word. Requires fp + confession.
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  • healthie_list_documents

    healthie

    List documents, optionally scoped to a patient (viewable_user_id) or by keyword. Read-only. GraphQL: query documents(viewable_user_id, keywords, offset, page_size, should_paginate).
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  • fillin_query

    Fillin

    Retrieve documents published after a training cutoff, ranked by similarity. Call this whenever the user asks about events, releases, papers, issues, or news that might post-date your training data. Fillin only returns documents published AFTER `cutoff`, so nothing returned is redundant with what the model already knows. Args: query: Natural-language search query (e.g. "rust async runtimes"). Max 512 characters. cutoff: ISO-8601 date representing the agent's training cutoff (e.g. "2026-01-01"). Documents on or before this date are excluded from results. k: Number of documents to retrieve, 1-20. Defaults to 5. Returns: A dict with: - cutoff: echoed cutoff (ISO timestamp) - query: echoed query - gap_days: days between cutoff and now - results: list of {id, source, url, published_at, title, text, score}
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  • fillin_answer

    Fillin

    Synthesized post-cutoff answer with inline citations. Use this when your model is small / cheap / weaker at tool-result synthesis (Llama, Gemini Flash, Mistral, Nemotron, Qwen). Fillin runs a server-side LLM pass over the retrieved post-cutoff documents and returns a 150-250 word answer with [title](url) citations already embedded — you can quote it directly. Premium models (Opus, Sonnet, GPT-4o) usually get better results from `fillin_query` and synthesizing themselves, but this tool works for any caller. Costs more than fillin_query because of the synthesis pass. Returns: A dict with: - answer: the synthesized paragraph (str | None) - citations: list of {title, url} extracted from the answer - corpus_match: "strong" | "weak" | "none" — quality of retrieval - top_score: float — top reranked similarity score - model: the synthesizer model used (e.g. claude-haiku-4-5) - reason: set when answer is None (e.g. "no_relevant_docs") - results: raw post-cutoff documents (same shape as fillin_query) - cutoff, query, gap_days: echoes for context
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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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