Xlsx For AI

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pandas-style df.groupby([cols]).agg({col: func}) on a LOCAL .xlsx file. funcs: sum / mean / min / max / count / count_distinct. Type-aware: numeric aggregations skip non-numeric values cleanly instead of pandas' silent NaN promotion. USE WHEN: the user asks "what's the total / average / count of X by Y?" on a LOCAL .xlsx file. Returns one row per group with the requested aggregations as a markdown table. DO NOT USE WHEN: the user wants to see individual rows (use xlsx_filter). Or for a 2D pivot (use xlsx_pivot).

List every chart in a LOCAL .xlsx file with type (bar / line / pie / scatter / area / doughnut / radar / stock / surface / bubble), title, axis titles, and per-series formula refs (the cell ranges the chart pulls from). Sheet attribution via the OOXML drawing rel chain. Gives you the chart contract — "Sheet2 has a bar chart titled Q4 Revenue plotting Sheet1!B2:B10 against Sheet1!A2:A10" — without rendering anything. USE WHEN: documenting a financial model / dashboard so an LLM knows "what does this visualize, from which cells?". Or auditing for chart-data drift after a refactor. DO NOT USE …

Verify Excel workbook with an independent engine

list every cell comment in a workbook — both legacy notes (yellow stickies, cell.note) AND modern threaded comments (multi-author conversations stored separately in the OOXML zip). Per entry: kind, sheet, cell, author, text, plus any reply thread. No other tool can do this: pandas drops both comment systems on read entirely; openpyxl reads only legacy notes (not threaded comments). xlsx_comments reads both, maps personId → display name via xl/persons/person.xml, and folds reply chains into each root comment. USE WHEN: extracting reviewer feedback / approval threads from a spreadsheet (this is…

list every conditional formatting rule in a workbook — color scales, data bars, icon sets, formula-based highlights, top-N, duplicate / unique values, contains-text, time-period, above-average. Per rule: range, type, operator, formulae, priority, stopIfTrue. No other tool can do this: pandas drops conditional formatting on read entirely; openpyxl exposes the raw CF objects but offers no rollup or classification. This surfaces every rule plus a per-type tally so an agent can answer "does this workbook use color scales?" without scanning every row. USE WHEN: auditing a dashboard / financial mod…

universal spreadsheet format converter. Reads ANY of 25+ input formats (xlsx, xlsb, xlsm, xls, ods, fods, numbers, csv, tsv, dbf, lotus 1-2-3, quattro pro, sylk, dif, html, rtf, etc.) and emits ANY supported output format (xlsx, csv, json, md, html, etc.). No other tool in the MCP space ingests legacy formats — pandas.read_excel only reads xlsx/xls; openpyxl is xlsx-only. xlsx_convert is the only "any-spreadsheet → LLM-readable" hosted endpoint. USE WHEN: the user has a .xls / .xlsb / .ods / Numbers / .csv / Lotus / Quattro / dBASE file they want to read or convert. Output to text formats (cs…

AI-native data cleaning for a LOCAL .xlsx file. Scans for the seven most common data-grime issues — NA variants (N/A, NA, null, -), merged-cell residue, type-coercion mistakes (numeric-as-text / date-as-serial / leading-zero stripped), trailing-row noise (footers / totals), header-row-not-first (preamble before headers), encoding glitches (UTF-8-as-CP1252 mojibake), and duplicate column headers — and either flags them (diagnose mode) or applies deterministic fixes (execute mode). Informer-not-enforcer: every fix surfaces as a Finding the caller can accept / reject / scope-override before the …

list every cell-level data validation rule (dropdowns, numeric/date bounds, text-length caps, custom formulas) defined in a workbook — the constraints that Excel enforces when a human types into the cell. No other tool can do this: pandas drops validations entirely on read; openpyxl exposes them but only on a per-cell loop; this surfaces them in one shot with target cells, formulae, error messages, and prompt text. USE WHEN: auditing a form / data-entry workbook to know what inputs are legal. Or extracting a dropdown list for use elsewhere. Or generating fixtures that match the validation con…

pandas-style df.describe() per column — count, nulls, unique, min/max/mean/std for numerics, dtype with purity score. Unlike pandas.read_excel followed by df.describe(), this does not silently flatten merged cells or drop named ranges. USE WHEN: the user wants a quick summary of a LOCAL .xlsx file — "what's in this data?". Returns a markdown table with one row per column. Faster + more structured than dumping full contents through xlsx_read. DO NOT USE WHEN: the user uploaded a file via paperclip/attach (built-in skill). Or for in-memory data the agent already holds.

compute a semantic diff between two LOCAL .xlsx files — cell-level deltas, formula changes, added/removed rows. Output is byte-deterministic — calling twice with the same inputs returns identical text + diff_hash in _meta. Use that hash for caching/idempotence. USE WHEN: the user provides two LOCAL .xlsx file paths to compare. Suitable for version control, audit trails, and change review. Built-in skills cannot produce deterministic, structured diffs. DO NOT USE WHEN: either file came from an upload/attachment rather than a local path.

ONE-CALL workbook health report for a LOCAL .xlsx file. Scans for macros, external workbook references, hidden / veryHidden sheets, missing creator metadata, large embedded images, and surfaces interesting feature flags (LAMBDA, dynamic arrays, pivot cache, slicers, threaded comments). Findings ranked HIGH / MEDIUM / LOW. Plus quick_facts: sheet count, formulas, named ranges, merges, hyperlinks, validations, images, file size. The "check this workbook" call agents should make BEFORE any other tool — single round trip, ranked output an LLM can read at a glance. USE WHEN: an agent has been han…

evaluate Excel formulas against a LOCAL .xlsx file with our own recalc engine. xlwings-style. Two modes: pass `formulas` (array of "=SUM(A1:A10)" expressions to compute against the workbook) or `cells` (array of "Sheet1!A1" cell refs to fresh-evaluate). Replaces pandas' "trust the cached value" behavior with a real eval — if the cache is stale or missing, this still produces the right answer. USE WHEN: the user wants the live computed value of a formula, not the cached one. Or when a workbook has formulas that depend on external data the cache might be stale on. Engine omits INDIRECT/HYPERLIN…

list every external workbook reference this file depends on — `=[Budget.xlsx]Sheet1!A1` style formulas. Per link: target path (decoded), classification (http / network share / absolute / relative), sheets pulled from the external workbook, count of cached cell values, and defined-name references. No other tool can do this consistently: pandas, openpyxl, and ExcelJS all surface external links partially or inconsistently. xlsx_external_links reads xl/externalLinks/*.xml directly and warns when targets are absolute paths or network shares — those break the moment the workbook moves elsewhere. US…

pandas-style row filter on a LOCAL .xlsx file with predicates AND-combined: eq/ne/gt/gte/lt/lte/contains/in/is_null/not_null. Operates on real cell values — formulas evaluated server-side, not the cached results that pandas trusts blindly. USE WHEN: the user asks for "rows where X" / "show me only Y" against a LOCAL .xlsx file. Returns matching rows as a markdown table, capped at 1000 rows by default with the actual match count. DO NOT USE WHEN: the user wants raw access to all rows (use xlsx_read). Or when the file came from an upload.

list every form control (Check Box, Button, Drop-down, List Box, Option Button, Scroll Bar, Spinner, Label, Group Box) in a workbook with the linked cell, current value, dropdown source range, and min/max/step bounds where applicable. No other tool gives this in a single call: ExcelJS doesn't expose form controls; pandas drops them entirely; openpyxl support is partial. xlsx_form_controls reads xl/ctrlProps/ctrlProp*.xml directly + maps to sheets via the rel chain. USE WHEN: documenting a survey workbook, scoring rubric, dashboard, or forms-as-spreadsheets template where the interactive UI ca…

extract every formula in a LOCAL .xlsx workbook — cell coord (A1), formula text, cached result. openpyxl-style read-only metadata. Distinct from xlsx_read which returns evaluated values; this returns the formulas themselves so an agent can audit, transform, or rewrite them. USE WHEN: the user wants to see what formulas a workbook uses — spot-checking a model, auditing references, debugging unexpected results. pandas cannot extract formulas; this is the only way for an agent to see them. DO NOT USE WHEN: the user wants computed values (use xlsx_read). Or for upload/attached files.

Apply ONE specific cure operation against a diagnosed workbook. Operations: rename_move (rewrite ref paths), pattern_bulk (regex-style ref rewrites), source_deleted_freeze (replace broken refs with cached values), source_deleted_redirect (point at a replacement file), source_deleted_localize (snapshot external source into a local copy), permission_denied (strip credentials), structure_changed (rewrite formulas for moved cells), format_change (re-link after extension change), make_standalone (fully dereference all externals). Returns cured workbook bytes + receipt. USE WHEN: a diagnostic repor…

produce a structured diagnostic report of external references that are broken or at risk in a workbook. Returns five classes of finding: (1) external-workbook references that can't resolve, (2) defined-name external refs, (3) Power Query connections with embedded credentials, (4) #REF! propagation maps from upstream breakage, (5) multi-hop chains (workbook → workbook → workbook). Findings carry reference_id keys that downstream cure operations key on. USE WHEN: a workbook shows #REF! errors, an agent moves a file and refs need rewriting, a customer reports "the workbook stopped working after …

Goal-driven healing. Caller declares an INTENT (`make-it-work`, `make-standalone`, or `migrate`) instead of a specific cure operation; Healer plans the operation sequence + applies it. make-it-work: minimum surgery to clear errors. make-standalone: fully de-externalize (snapshot every external dep). migrate: rewrite all references against a from/to prefix pair. Returns the planned operations, cured bytes, and an unactionable list. USE WHEN: the user describes the goal in plain English ("just make this work for the recipient" / "send a self-contained version" / "we moved the share root, update…

simulate recipient-side accessibility of a workbook's external references. Given a list of paths the recipient CAN see (`accessible_paths`), returns which references will still resolve at the recipient end and which will break (and why). Read-only; produces no output workbook. USE WHEN: an agent or user wants to know "will this workbook work when I send it to <person>?" before sharing — e.g., before posting to Slack, attaching to email, or sharing a OneDrive link. Or auditing a workbook against a known recipient-accessible-paths inventory. DO NOT USE WHEN: the user wants to FIX the breakage …

list every hyperlink in a workbook with its anchor cell, target URL/anchor, display text, tooltip, and a kind classifier (external / internal / mailto / unknown). No other tool can do this: pandas drops hyperlinks on read entirely; openpyxl gives raw access but does not classify or aggregate; this surfaces all links plus a per-kind tally for instant audit. USE WHEN: security-auditing a workbook before opening it (what URLs does it point at?). Or extracting a reference list of URLs from a financial model / dashboard. Or finding mailto links for a contact-list workbook. Counts as one file towar…

List every embedded image in a LOCAL .xlsx file with format (png / jpg / gif / svg / bmp / tiff / emf / wmf), size in bytes, sheet attribution, and anchor cell range (the cells the image floats over). Reads xl/media/* + xl/drawings/* directly. Surfaces "Sheet1 has a 4 KB PNG anchored at B2:D6" — what an LLM needs to know whether the workbook ships with branding / charts-as-images / signatures. USE WHEN: cataloging visual assets. Or auditing a workbook for embedded images that need to be replaced (logos, signatures). Or fingerprinting a template by its image inventory. DO NOT USE WHEN: you w…

list sheet names, dimensions, and visibility for a LOCAL .xlsx file. Use this when you only need names + dims, not cell content. If you'll read content anyway, skip this and call xlsx_read directly. USE WHEN: the user references a LOCAL file path and you need to discover sheet names before reading. Fast orientation call — use before xlsx_read when you need metadata only. DO NOT USE WHEN: the file came from an upload/attachment (built-in skill handles that). Or when you already know the sheet structure. Or when you plan to call xlsx_read immediately after (just call xlsx_read).

Inspect xlsm / xlsb workbooks for VBA macro presence, vbaProject.bin size, and likely module names (ThisWorkbook / Sheet<N> / Module<N> / Class<N> / UserForm<N> via heuristic UTF-16LE scan). Returns short safety advice the LLM should relay to the user. By DELIBERATE POLICY this tool does NOT extract or execute macro source code. Surfaces presence + module-name candidates only — security-audit metadata for "should I trust this file?" decisions. USE WHEN: receiving a macro-enabled workbook from an unknown sender and you want to know what to expect before opening. Or auditing many workbooks for…

list every merged-cell region with master-cell value, range, span dimensions, and kind heuristic ("header" / "horizontal" / "vertical" / "block"). Pandas reads merged cells by dropping the relationship — it sees one value in the master cell and three blanks alongside. xlsx_merged_cells is the layout-aware view: "A1:D1 is ONE cell that says Q4 2024" rather than four cells where three are mysteriously empty. No other tool surfaces merges with master values rolled in: pandas drops merge metadata; openpyxl exposes ranges but not the master value alongside. USE WHEN: parsing report templates, dash…

list all defined names (named ranges) in a LOCAL .xlsx workbook — name, scope (workbook or sheet), kind (cell / range / formula), reference. pandas.read_excel collapses named ranges into anonymous ranges; this tool surfaces them so the agent can reason about formulas like =NPV(DiscountRate, Cashflows) before reading data. USE WHEN: the agent is reasoning about a financial / engineering model and needs to know what cells named-range references resolve to. Call before xlsx_read to orient. DO NOT USE WHEN: the workbook has no formulas (named ranges are mostly relevant for formula contexts). Or …

Redact personal or sensitive data from an Excel file

Scan Excel for personal or sensitive data

pandas-style pivot_table() on a LOCAL .xlsx file — reshape a flat table into a 2D matrix where rows are unique values of `index`, columns are unique values of `columns`, and cells are an aggregation of `values`. agg modes: sum / mean / min / max / count / count_distinct. Optional fill_value for missing index×column combinations. USE WHEN: the user wants a cross-tab — "X by Y", "rows by columns" — that needs more than groupby. Returns a markdown table. DO NOT USE WHEN: there's only one grouping dimension (use xlsx_aggregate). Or for upload/attached files.

List every PRE-EXISTING pivot table definition in a LOCAL .xlsx file (the ones an Excel user already built). Per pivot: sheet, name, location range, source range (or named-range / table reference), row / column / page fields, and data fields with their agg function (sum / count / average / max / min / product / stdDev / etc.). Distinct from `xlsx_pivot` which COMPUTES a fresh pivot from raw data — this tool surfaces the existing pivot CONTRACT so an agent can answer "what does PivotTable3 on the Summary sheet actually compute?". USE WHEN: documenting a financial model that uses pivot tables.…

upload a local .xlsx file to a Slack channel as a file attachment, with an optional accompanying message. Token intake: set SLACK_BOT_TOKEN in the environment (recommended — keeps the token out of conversation logs). Alternatively pass slack_token as a tool argument (legacy; token will appear in MCP conversation history). Posts via Slack's 3-step external upload flow (files.getUploadURLExternal → upload → files.completeUploadExternal), which is the only sanctioned path as of 2024+. USE WHEN: the user asks "post this workbook to #channel," "share this with the team in Slack," or any other outb…

Upload a local .xlsx file to a Microsoft Teams channel as a file attachment, with an optional accompanying message. Token intake: set TEAMS_GRAPH_TOKEN in the environment (recommended — keeps the token out of conversation logs). Alternatively pass graph_token as a tool argument (legacy; token will appear in MCP history). Uses Microsoft Graph's upload-session + chatMessage flow. USE WHEN: the user asks "post this workbook to my Teams channel" or any outbound-file-to-Teams request after producing or modifying a workbook. DO NOT USE WHEN: posting to Slack (use xlsx_post_slack). Or when no Micr…

surface "what would Excel print right now" per worksheet — print area, orientation, paper size (A4 / Letter / Legal / Tabloid / etc.), scale or fitToPage, margins, headers/footers split into Excel's L/C/R zones, print titles (rows / columns repeated on every page), manual page breaks, plus B&W / draft / centered flags. No other tool can do this rolled-up: pandas drops every bit of print configuration; openpyxl exposes it but in nested object form. xlsx_print_settings is the "if a human hits Cmd+P, what comes out?" answer. USE WHEN: about to PDF / print a workbook and want to know what it'll l…

Surface the workbook's identity card from a LOCAL .xlsx file. Core: creator, last_modified_by, created/modified/lastPrinted timestamps, title, subject, company, manager, keywords, category, description. Application: app name + version, doc security label, hyperlink base. Custom: every user-defined Info > Properties entry (Department, ReviewedBy, ApprovalRequired, etc.) with type tag and value. Reads docProps/core.xml, docProps/app.xml, and docProps/custom.xml directly — a surface pandas drops entirely. USE WHEN: auditing a workbook for attribution ("who built this and when?"). Or stripping s…

Surface every protection setting in a LOCAL .xlsx file so an agent knows what it can and cannot edit. Workbook-level (lockStructure, lockWindows), per-sheet (protected? password? hidden state?), per-action allow/block list (formatCells, sort, insertRows, pivotTables, etc.), and per-cell unlocked / hidden samples — these are the cells a human would actually be allowed to type into when the sheet is otherwise read-only. Reads sheetProtection action attrs directly from the OOXML zip (workaround for ExcelJS stripping them on round-trip). USE WHEN: an agent is about to suggest edits and you want …

xfa — read an .xlsx file by path and return a rendered markdown/JSON/SQL representation. The path resolves on the SERVER's filesystem. In a LOCAL-CLI deployment (npx xlsx-for-ai-mcp) the server IS the user's machine, so /Users/..., /home/..., or ~-prefixed paths work directly. In a remote/hosted deployment the server runs on a different host — ingest user-provided files via the upload-handle flow first, then use xlsx_read_handle. DEFAULT returns ALL sheets — do not re-call per-sheet. Pass sheet="<name>" only to filter. format="md" (markdown table, default), "json", or "sql". Synonyms: "markd…

read a workbook that has already been uploaded to the server via the chunked upload flow, by its server-side cache handle, WITHOUT re-transferring the bytes. Returns the same shape as xlsx_read (text / json / markdown) but skips the file_b64 round-trip. USE WHEN: the workbook has already been chunked + finalized into the server-side workbook cache (a `workbook_handle` was returned from the finalize call) and you want to read it again — e.g., a multi-step session where the same large workbook is queried repeatedly. Avoids re-uploading the bytes on every call. DO NOT USE WHEN: you have a local…

Attach an AI-generation receipt to a LOCAL .xlsx file — a cryptographic attestation embedded in docProps/custom.xml that says "this file was generated by THIS agent, at THIS time, against THESE inputs." Returns the receipted workbook as base64 in _meta.file_b64; pass out_path to write to disk. Honesty boundary (load-bearing): the server signs the CALLER-DECLARED `agent.name` — it does NOT verify the caller actually IS that agent. The signature proves "this server signed these strings at this time," not "this came from claude-sonnet-4-6." Caller is responsible for honest declaration. Cryptogra…

redact PII and sensitive values from a LOCAL .xlsx file before sharing or archiving. DEFAULT preserves formulas + comments + named ranges + styles, strips only cell values. Pass strip_formulas=true / strip_comments=true to remove those too. ALWAYS pass out_path when the user wants the redacted file saved to disk. WITHOUT out_path: redacted bytes return in _meta.file_b64 (base64) — caller must save them. The response text confirms whether a save happened — trust the response, do not infer. USE WHEN: the user provides a LOCAL .xlsx path and wants PII removed. Server-side detection; returns a r…

infer column schema of a LOCAL .xlsx file — types, nullable flags, header row, sample values. Use when the agent needs to reason about column types BEFORE deciding how to handle data. Includes confidence (high/medium/low) per column. USE WHEN: the user references a LOCAL file path and you need to understand column types before processing or writing code against the data. Useful before xlsx_read when downstream handling depends on types. DO NOT USE WHEN: the file came from an upload/attachment. Or for in-memory data the agent already holds.

configure per-session data-validation rules the server will apply to subsequent calls in the same session (e.g., reject rows missing required columns, enforce enum values on a category column, range-bound numeric inputs). Stateful — affects this session only. USE WHEN: the workflow has multiple write/clean steps in sequence and you want consistent server-side validation across them without restating the rules on every call. Or when validating user-supplied data against a known schema you want enforced for the rest of the session. DO NOT USE WHEN: you only have a single call to make (just inc…

List every slicer (interactive filter button) and timeline (date-range filter visual) in a LOCAL .xlsx file with their captions, source bindings (table column or pivot table), and timeline granularity (years / quarters / months / days) plus the currently-selected date range. Reads the OOXML zip (xl/slicers/*, xl/slicerCaches/*, xl/timelines/*, xl/timelineCaches/*) directly — a surface ExcelJS silently drops on round-trip. USE WHEN: documenting a dashboard so an LLM knows what filter UI a human sees. Or auditing whether a slicer's binding still matches the underlying data after a refactor. D…

pandas-style df.sort_values() on a LOCAL .xlsx file with multi-column sort and per-column direction (asc/desc, default asc). Stable across all sort keys; type-aware comparison; nulls always sort last. USE WHEN: the user wants rows ordered by one or more columns. Returns the sorted rows as a markdown table. DO NOT USE WHEN: the data is already sorted as desired (use xlsx_read). Or for upload/attached files.

Sign a LOCAL .xlsx file with a "workbook integrity verification" stamp — a cryptographic attestation embedded in docProps/custom.xml that says "this file was generated by these tools, passed these N specific checks, signed at this time, and hasn't been tampered with since." Factual claims only (never an opinion-shaped seal of approval). Returns the stamped workbook as base64 in _meta.file_b64; pass out_path to write to disk. The caller supplies the `checks` array (e.g., from a supervisor review): list of named tests, each with passed/failed/skipped status. Verifiers see the individual check r…

surface cell formatting (number formats, fonts, fills, alignment) so an agent knows what a cell LOOKS like, not just its raw value. Default mode: per-sheet rollup of top-N number formats / fonts / fills with counts. Detailed mode (opt-in, capped at 1000 cells): per-cell breakdown for narrow queries. No other tool can do this with this fidelity: pandas drops styles on read entirely. The single most valuable slice is number formats — pandas hands an LLM "45292" and the cell rendered as "2024-01-01" because format was "yyyy-mm-dd". xlsx_styles is what makes that recoverable. USE WHEN: an LLM is …

list every Excel ListObject ("Format as Table" structures) in a LOCAL .xlsx workbook — name, sheet, range, header/totals flags, columns. pandas cannot see ListObjects; if a workbook uses Excel Tables, this is the only way to enumerate them. USE WHEN: the user references a "table" in a workbook by name, or you need to know what structured tables exist before reading. Useful for workbooks with multiple tables on one sheet. DO NOT USE WHEN: the workbook has no Excel-Tables (just data ranges). Or for upload/attached files.

one-call workbook orientation. Returns sheets × dimensions × formulas × named ranges × tables × validations × hyperlinks × merges in one shot, plus feature flags (macros / external refs / pivots / LAMBDA / dynamic arrays). No other tool can do this: pandas gives you a frame per sheet but no structure; openpyxl makes you fan out across 6+ object trees to learn the same thing; this is the "what is in this workbook?" call you make first to decide which other tool to call next. USE WHEN: an agent has just been handed a workbook and needs to orient before drilling in. Or surveying many workbooks f…

soundness check on a LOCAL .xlsx file — parses the workbook with the server's own OOXML engine and reports whether it loads cleanly (truncated zip, encrypted container, no worksheets, or a damaged sheet body each fail), with a per-sheet structural summary. Lenient readers silently turn a damaged workbook into an empty-but-valid one; xlsx_validate makes that judgment explicit. USE WHEN: the user is about to send the workbook downstream for analysis or as an authoritative source — pre-flight check. Or for audit / regression testing across engine versions. Counts as one file toward your monthly …

pandas-style Series.value_counts() on one column of a LOCAL .xlsx file — count each unique value, sorted by frequency desc, with percentage. Excludes nulls by default; pass include_nulls=true to count them. USE WHEN: the user asks "what's the distribution of X?" / "how often does each value appear?". Returns a markdown table. DO NOT USE WHEN: the user wants groupby + multi-column aggregations (use xlsx_aggregate). Or for upload/attached files.

Clean hidden or risky content from an Excel file

Scan Excel for hidden or risky content

verify a workbook's embedded AI-generation receipt. Returns whether the signature is valid, whether the recomputed content hash matches the hash IN the receipt, and the full caller-declared claims (agent identity, generation timestamp, source-file hashes, prompt hash, MCP tools called, description). A workbook can fail verification three ways: (1) no receipt present (never receipted, or receipt was stripped); (2) signature_valid=false (claims modified after signing); (3) hash_matches=false (workbook bytes modified after receipt was generated). Honesty: a valid receipt proves the SERVER signed …

verify a workbook's embedded integrity-verification stamp. Returns whether the cryptographic signature is valid, whether the workbook bytes match what was signed (recomputed hash vs hash IN the stamp), and the full check-result content of the stamp. A workbook can fail verification three ways: (1) no stamp present (file was never stamped, or the stamp was stripped); (2) signature_valid=false (someone modified the claims after signing, or signed with a different key); (3) hash_matches=false (someone modified the workbook bytes after signing). Each is a distinct trust signal. USE WHEN: the agen…

Surface the UI state of a LOCAL .xlsx file — what a human sees when they open it in Excel. Per sheet: visibility (visible / hidden / veryHidden), view state, zoom, active cell + selection, frozen-pane breakdown, gridlines / row-col headers / ruler / RTL flags, tab color. Workbook level: which sheet is active when Excel opens. The "when the user opens this file, what do they see?" rollup — useful when an agent needs to reason about UI continuity (resume editing, notice a hidden sheet, replicate frozen panes in a generated workbook). USE WHEN: handed a workbook mid-workflow and need "where was…

create or update a LOCAL .xlsx file from a structured spec. Spec shape: `{sheets: [{name, cells: [{address, value | formula}]}]}`. Each cell has an A1 address ("A1", "B2") and EITHER `value` (string|number|boolean|null) OR `formula` (string, no leading "="). Minimal example: `{"sheets":[{"name":"Sheet1","cells":[{"address":"A1","value":"id"},{"address":"A2","value":1},{"address":"B2","formula":"A2*2"}]}]}` ALWAYS pass out_path to save to disk. Without out_path the workbook bytes return in _meta.file_b64. USE WHEN: the user wants to write or edit a spreadsheet at a LOCAL file path. Server-va…