"How to Work with Excel and Word Documents" matching MCP tools:

• 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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• search_content

  flevy

  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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• search

  Islam West Africa Collection (IWAC)

  Search the Islam West Africa Collection across newspaper articles, Islamic publications, archival documents, academic references, audiovisual recordings, photographs, and the authority index (persons/places/organisations/events/subjects). Pass ONE concept or name — e.g. 'Tijaniyya', 'laïcité', 'Sheikh Gumi', 'pèlerinage'. Matching is accent- and case-insensitive; a multi-word query requires every word to appear somewhere in the item, so prefer a single concept per call. Write query strings and concept keywords in French for press/publication/document/index discovery even when the user's report language is not French. Academic references are multilingual, so try French and English title/abstract terms when relevant; metadata/filter labels remain French. Use the French transliteration of Islamic terms (Tabaski not 'Eid al-Adha', charia not 'sharia', Maouloud not 'Mawlid'). Returns {results:[{id,title,url,category}], ranking}; each result's `category` names its subset and the `ranking` field documents the ordering. Pass an id to `fetch` to read the full text. For filtered queries (by country, date, or newspaper) use the search_* tools instead.

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• transcribe

  Weftly

  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.

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• word_create

  local-mcp

  Creates a new Word (.docx) document at `path` with the given text content (and an optional title rendered as the heading). Requires confirm=true — called without it, returns a preview of what will be written instead of creating the file. The path must be somewhere Local MCP can write; Desktop/Documents/Downloads may need a one-time Files-and-Folders grant (System Settings → Privacy & Security → Files and Folders). Returns {created, path}. For a OneDrive or Google Drive path use onedrive_write_file / gdrive_write_file; to append to an existing doc use word_append, to read one word_read.

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• get_player_template

  turnale

  A blank Excel workbook the organiser fills in and hands back: Name, Level, Gender, Comments, one player per row, with a sheet explaining each column. Offer it when the organiser has no list ready, asks how to send their players, or would rather work in a spreadsheet than paste names into chat. Needs no key. The file comes back both as a download link and as an attachable file. Reading a filled-in sheet needs no tool: parse it yourself and send the rows to add_players.

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• get_about

  cricket-travel

  Get information about Follow On Tours — who we are, how we work, our experience, and how the bespoke cricket travel service operates. Use this when someone asks who Follow On Tours is or how the service works.

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• get_pm_playbook

  AIOProductOS MCP

  How to operate as a product manager on AIOProductOS. No arguments and no side effects — returns the same operating guide as plain text every call (deterministic): how to ground in the product brain, keep work welded to the spine (insight→feature→task→outcome), prioritise on evidence (affected accounts + MRR + reach), and what 'done' means. Call it FIRST, before planning or prioritising, to load the house rules the other tools assume.

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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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• lemma_get_schema

  mcp

  Retrieve a Lemma schema by its ID via GET /v1/schemas/{id}. A schema declares how documents of a given type are interpreted and normalized. Returns SchemaMeta { id, description? } with additionalProperties open — implementations commonly include a `normalize` artifact (WASM that maps raw documents to canonical form) and its content hash. Use this when you need to interpret attribute keys returned by lemma_query_verified_attributes.

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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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• info

  systemprompt.io reports

  Free usage guide for this server. Explains how the paid report tools work: exact input requirements, per-call pricing, and how to complete payment via x402 (USDC on Base) or Stripe checkout. Costs nothing and never returns a 402. Call this first before any paid tool.

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• file_write

  local-mcp

  Writes text to a local file — create, overwrite, or append. For .txt/.md/.csv/.json/.log and any plain-text or code file. (For Word use word_create, Excel excel_create, PowerPoint ppt_create.) The path must be inside an allowed folder — the same allowlist as file_read (home directory by default; extend via Advanced Settings → Allowed folders). Overwriting an existing file requires confirm=true (the first call returns a preview instead); append=true adds to the end and never needs confirm. Missing parent folders are created.

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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_kb

  DeskCrew

  Read-only full-text search over this tenant’s PUBLISHED knowledge-base articles (playbooks, policies, how-tos); unpublished drafts are never returned and the tenant is fixed by your credentials. Reach for this FIRST to ground an answer in official, tenant-specific guidance before replying to a customer or drafting a resolution. Returns articles ranked by relevance, each with its id, title, a highlighted snippet, and updatedAt: search uses AND semantics, so every word in the query must match. [free]

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• get_guide

  Tu Lugar

  Curated TuLugar guides (general education, kept current): buying-process (step-by-step + documents), foreigners (rights + restrictions for non-Paraguayans), closing-costs (what fees exist), renting (contracts, deposits, garante), publishing (listing tips), airbnb (short-term rental basics). ALWAYS use this for "how does buying/renting work" / process / documents questions — the content IS in scope to share; only personalized legal advice is not.

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• get_guide

  Tu Lugar

  Curated TuLugar guides (general education, kept current): buying-process (step-by-step + documents), foreigners (rights + restrictions for non-Paraguayans), closing-costs (what fees exist), renting (contracts, deposits, garante), publishing (listing tips), airbnb (short-term rental basics). ALWAYS use this for "how does buying/renting work" / process / documents questions — the content IS in scope to share; only personalized legal advice is not.

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• ado_read_attachment

  ALM X++ MCP Server

  AZURE DEVOPS ONLY -- Reads the ACTUAL CONTENT of a file attached to a work item (Excel spreadsheet, Word document, text/CSV/JSON/XML file, or image). WHEN: a work item (FDD/RDD/CR/Bug/Task/User Story) has an Excel/Word attachment with requirements, field mappings, mockups, or specs that need to be read to understand the ask. Triggers: 'read the attachment', 'open the excel file on the work item', 'what does the attached document say', 'lis le fichier joint', 'ouvre l'excel du ticket'. Call ado_analyze_workitem first (or ado_query_workitems) to discover attachment file names if you don't already know the exact fileName. Supported: .xlsx/.xlsm (returns sheet names + a markdown table of the requested/first sheet), .docx (returns extracted markdown text + tables), .txt/.csv/.json/.xml/.md/.log (returned as-is), images (.png/.jpg/.jpeg/.gif/.bmp/.webp, returned as a base64 data URI for visual analysis, max 4 MB). Other binary formats (PDF, .pptx, .zip, etc.) are NOT parsed -- returns metadata + a manual download link instead. Max attachment size read: 25 MB. Requires DEVOPS_ORG_URL + DEVOPS_PAT env vars.

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• combined_trends_tool

  Santiment

  Combined trends tool that fetches trending words, stories, and documents in parallel. This tool provides a unified view of all trending data - words with their documents and stories - in a single response across all crypto projects. ## When to use vs `trending_stories_tool` This is a superset of `trending_stories_tool`: same stories, plus trending words, their context and AI-generated bull/bear summaries. It calls an LLM, so it is slower and has a tighter per-tool rate-limit sub-cap than every other tool. If only trending stories are needed, call `trending_stories_tool` instead; set `include_words: false` / `include_stories: false` to drop a half that is not needed. Do not call both tools for the same question. ## Parameters - `time_period` - Time period for trending data (e.g., '1h', '6h', '1d', '7d'). Defaults to '1h' (last hour). - `size` - Number of items per category to return (max 30). Defaults to 10. - `include_stories` - Include trending stories in response. Defaults to true. - `include_words` - Include trending words in response. Defaults to true. ## Response - `trends` - Combined trending data containing stories and words. - `metadata` - Request metadata including time period, size, and included data types. - `errors` - Any non-fatal errors encountered during data fetching. ## Trending Data Structure ### Stories - `title` - Title of the trending story. - `summary` - Summary of the story. - `score` - Trending score. - `query` - Search query used to find the story. - `related_tokens` - List of related crypto tokens (format: "BTC_bitcoin"). - `bullish_sentiment_ratio` - Bullish sentiment ratio. - `bearish_sentiment_ratio` - Bearish sentiment ratio. ### Words - `word` - The trending word. - `score` - Trending score. - `slug` - Associated project slug (if word is project-related). - `summary` - AI-generated summary of discussions. - `bullish_summary` - Summary of bullish sentiment. - `bearish_summary` - Summary of bearish sentiment. - `positive_sentiment_ratio` - Positive sentiment ratio. - `negative_sentiment_ratio` - Negative sentiment ratio. - `neutral_sentiment_ratio` - Neutral sentiment ratio. - `positive_bb_sentiment_ratio` - Positive bull/bear sentiment ratio. - `negative_bb_sentiment_ratio` - Negative bull/bear sentiment ratio. - `neutral_bb_sentiment_ratio` - Neutral bull/bear sentiment ratio. - `context` - Related words that appear with this trending word. - `documents_summary` - AI-generated summary of related social media discussions.

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• get_about

  Follow On Tours

  Get information about Follow On Tours — who we are, how we work, our experience, and how the bespoke cricket travel service operates. Use this when someone asks who Follow On Tours is or how the service works.

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