Xlsx For Ai MCP Server

xlsx-for-ai

Short name: xfa — a real CLI command (xfa <file>, xfa samples, xfa --version) and the prompt shorthand (e.g. "use xfa to read this file"). Same entrypoint as xlsx-for-ai; matches the internal xfa_* / XFA_* brand surface.

The missing reliability layer that makes spreadsheet reasoning production-grade for LLMs.

A thin npm client over a hosted API. Install once, add to your agent config, and your agent gets 50 production-grade tools for reading, writing, diffing, redacting, healing, and cryptographically attesting .xlsx files — engine complexity runs server-side, engine IP stays private.

npm install -g xlsx-for-ai

The global install puts the xlsx-for-ai-mcp binary on your PATH — that's what the canonical configs below point at. A pinned global install launches fast and works offline; upgrade with npm install -g xlsx-for-ai@latest when a new version ships.

Upgrading from 1.5.x? This is a re-architecture, not a feature bump: the heavy local engine is gone from the npm package. All rendering happens server-side. The cursor-reads-xlsx alias still works. See Migration below.

---

MCP configuration

Add the server to your agent runtime under the name xfa (so "use xfa to read this" resolves). First invocation auto-registers an anonymous client UUID — no email, no signup, no friction.

Claude Code

The global install auto-registers the xfa MCP server in ~/.claude.json — no extra step:

npm install -g xlsx-for-ai

If your environment skips install scripts (--ignore-scripts, CI, or a sudo install), register it manually:

claude mcp add xfa -- xlsx-for-ai-mcp

Verify: in a new Claude Code session, ask "what MCP tools do you have?" — 50 xlsx_* tools should appear, including xlsx_doctor (one-call health report — try it first on any unknown workbook).

Then run xfa samples (shorthand for xlsx-for-ai samples) to drop two demo workbooks in your working directory and get paste-ready prompts to try.

Cursor

Config file: ~/.cursor/mcp.json

{
  "mcpServers": {
    "xfa": {
      "command": "xlsx-for-ai-mcp"
    }
  }
}

Verify: open Cursor settings → MCP → confirm xfa shows 50 xlsx_* tools.

Continue

Config file: ~/.continue/config.json

{
  "mcpServers": [
    {
      "name": "xfa",
      "command": "xlsx-for-ai-mcp"
    }
  ]
}

Verify: restart VS Code, open the Continue panel, and check the MCP server list.

Codex CLI

Pass --mcp-server on the command line, or add to your Codex config:

{
  "mcpServers": {
    "xfa": {
      "command": "xlsx-for-ai-mcp"
    }
  }
}

Verify: run codex --list-tools and confirm 50 xlsx_* tools are listed.

Zed

Config file: ~/.config/zed/settings.json

{
  "context_servers": {
    "xfa": {
      "command": {
        "path": "xlsx-for-ai-mcp"
      }
    }
  }
}

Verify: open Zed's assistant panel — the xlsx tools should appear in the tool picker.

Windsurf

Config file: ~/.codeium/windsurf/mcp_config.json

{
  "mcpServers": {
    "xfa": {
      "command": "xlsx-for-ai-mcp"
    }
  }
}

Verify: open Windsurf → Cascade → settings, confirm xfa is listed as an active MCP server.

Custom agents / API

For custom MCP clients, the binary is xlsx-for-ai-mcp (stdio transport). Override the API base URL with the XLSX_FOR_AI_API env var for local dev against http://localhost:3000.

---

What it does

50 tools registered in tools/list. Descriptions are intentionally rich — an agent reading a transcript can tell what each tool does and when to reach for it, without extra docs.

Triage / orient

Read / write

Pandas-parity (compute fresh aggregates)

Structure-preservation — the moat (pandas drops every one of these on read)

Integrations

Integrity verification

Healer — external-reference breakage

Workbooks rot. A file moves and #REF! propagates through every dependent formula. A Power Query connection embeds credentials nobody can rotate. A defined name points at an external workbook that doesn't exist anymore. The healer family diagnoses these classes and applies targeted cures — read-only diagnosis, simulated-before-applied repair, and a high-level intent path when the agent doesn't want to spell out individual cure operations.

Tool responses include a citation footer and a _meta block (tool name, version, tier, request ID, powered_by). Both pass through verbatim; nothing is stripped.

---

Tools

All 50 tools the MCP server exposes (generated from tools/list). Invoke any by asking your agent in plain English, or call the API/CLI directly.

Read & explore

• xlsx_read — read an .xlsx file by path and return a rendered markdown/JSON/SQL representation.
• xlsx_read_handle — 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.
• xlsx_validate — cross-engine consistency check on a LOCAL .xlsx file — runs the workbook through TWO independent renderers (@protobi/exceljs and @cj-tech-master/excelts) and reports cell-level divergences.

Inspect structure

• xlsx_charts — 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.
• xlsx_comments — 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.
• xlsx_conditional_formats — 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.
• xlsx_data_validations — 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.
• xlsx_describe — pandas-style df.describe() per column — count, nulls, unique, min/max/mean/std for numerics, dtype with purity score.
• xlsx_external_links — 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.
• xlsx_form_controls — 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.
• xlsx_formulas — extract every formula in a LOCAL .xlsx workbook — cell coord (A1), formula text, cached result. openpyxl-style read-only metadata.
• xlsx_hyperlinks — list every hyperlink in a workbook with its anchor cell, target URL/anchor, display text, tooltip, and a kind classifier (external / internal / mailto / unknown).
• xlsx_images — 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.
• xlsx_list_sheets — list sheet names, dimensions, and visibility for a LOCAL .xlsx file.
• xlsx_macros — Inspect xlsm / xlsb workbooks for VBA macro presence, vbaProject.bin size, and likely module names (ThisWorkbook / Sheet / Module / Class / UserForm via heuristic UTF-16LE scan). Returns short safety advice the LLM should relay to the user.
• xlsx_merged_cells — 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.
• xlsx_named_ranges — list all defined names (named ranges) in a LOCAL .xlsx workbook — name, scope (workbook or sheet), kind (cell / range / formula), reference.
• xlsx_pivot_tables — 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.).
• xlsx_print_settings — 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.
• xlsx_properties — 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.
• xlsx_protection — 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.
• xlsx_schema — infer column schema of a LOCAL .xlsx file — types, nullable flags, header row, sample values.
• xlsx_slicers_timelines — 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.
• xlsx_styles — 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.
• xlsx_tables — list every Excel ListObject ("Format as Table" structures) in a LOCAL .xlsx workbook — name, sheet, range, header/totals flags, columns.
• xlsx_workbook_views — 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.

Query & analyze

• xlsx_aggregate — pandas-style df.groupby([cols]).agg({col: func}) on a LOCAL .xlsx file. funcs: sum / mean / min / max / count / count_distinct.
• xlsx_diff — compute a semantic diff between two LOCAL .xlsx files — cell-level deltas, formula changes, added/removed rows.
• xlsx_eval — evaluate Excel formulas against a LOCAL .xlsx file via HyperFormula. xlwings-style.
• xlsx_filter — 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.
• xlsx_pivot — 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.
• xlsx_sort — pandas-style df.sort_values() on a LOCAL .xlsx file with multi-column sort and per-column direction (asc/desc, default asc).
• xlsx_topology — 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).
• xlsx_value_counts — pandas-style Series.value_counts() on one column of a LOCAL .xlsx file — count each unique value, sorted by frequency desc, with percentage.

Clean & fix

• xlsx_data_clean — 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).

Convert

• xlsx_convert — 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.).

Write (new file)

• xlsx_redact — redact PII and sensitive values from a LOCAL .xlsx file before sharing or archiving.
• xlsx_write — create or update a LOCAL .xlsx file from a structured spec.

Integrity & verification

• xlsx_healer_cure — 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.
• xlsx_healer_diagnose — 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.
• xlsx_healer_intent — 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.
• xlsx_healer_simulate — 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.
• xlsx_receipt — 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.
• xlsx_stamp — 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.
• xlsx_verify_receipt — 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).
• xlsx_verify_stamp — 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.

Integrations

• xlsx_post_slack — upload a local .xlsx file to a Slack channel as a file attachment, with an optional accompanying message.
• xlsx_post_teams — Upload a local .xlsx file to a Microsoft Teams channel as a file attachment, with an optional accompanying message.

Session

• xlsx_session_set_validations — 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.

One-call capstone

• xlsx_doctor — 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.

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Functions

xlsx_eval recalculates formulas with HyperFormula v3.2.0 — 382 Excel functions across these categories:

• Math & trig (101) — ABS, ACOS, ACOSH, ACOT, ACOTH, ARABIC, ASIN, ASINH, ATAN, ATAN2, ATANH, AVERAGE, AVERAGEA, AVERAGEIF, CEILING, CEILING.MATH, CEILING.PRECISE, COMBIN, COMBINA, COS, COSH, COT, COTH, COUNT, COUNTA, COUNTBLANK, COUNTIF, COUNTIFS, COUNTUNIQUE, CSC, CSCH, DEGREES, EVEN, EXP, FACT, FACTDOUBLE, FLOOR, FLOOR.MATH, FLOOR.PRECISE, GCD, INT, ISO.CEILING, LCM, LN, LOG, LOG10, MAX, MAXA, MAXIFS, MIN, MINA, MINIFS, MOD, MROUND, MULTINOMIAL, ODD, PI, POWER, PRODUCT, QUOTIENT, RADIANS, RAND, RANDBETWEEN, ROMAN, ROUND, ROUNDDOWN, ROUNDUP, SEC, SECH, SERIESSUM, SIGN, SIN, SINH, SQRT, SQRTPI, STDEV, STDEV.P, STDEV.S, STDEVA, STDEVP, STDEVPA, STDEVS, SUBTOTAL, SUM, SUMIF, SUMIFS, SUMPRODUCT, SUMSQ, SUMX2MY2, SUMX2PY2, SUMXMY2, TAN, TANH, TRUNC, VAR, VAR.P, VAR.S, VARA, VARP, VARPA, VARS
• Statistical (108) — AVEDEV, BESSELI, BESSELJ, BESSELK, BESSELY, BETA.DIST, BETA.INV, BETADIST, BETAINV, BINOM.DIST, BINOM.INV, BINOMDIST, CHIDIST, CHIDISTRT, CHIINV, CHIINVRT, CHISQ.DIST, CHISQ.DIST.RT, CHISQ.INV, CHISQ.INV.RT, CHISQ.TEST, CHITEST, CONFIDENCE, CONFIDENCE.NORM, CONFIDENCE.T, CORREL, COVAR, COVARIANCE.P, COVARIANCE.S, COVARIANCEP, COVARIANCES, CRITBINOM, DEVSQ, ERF, ERFC, EXPON.DIST, EXPONDIST, F.DIST, F.DIST.RT, F.INV, F.INV.RT, F.TEST, FDIST, FDISTRT, FINV, FINVRT, FISHER, FISHERINV, FTEST, GAMMA, GAMMA.DIST, GAMMA.INV, GAMMADIST, GAMMAINV, GAMMALN, GAMMALN.PRECISE, GAUSS, GEOMEAN, HARMEAN, HYPGEOM.DIST, HYPGEOMDIST, LARGE, LOGINV, LOGNORM.DIST, LOGNORM.INV, LOGNORMDIST, LOGNORMINV, MEDIAN, NEGBINOM.DIST, NEGBINOMDIST, NORM.DIST, NORM.INV, NORM.S.DIST, NORM.S.INV, NORMDIST, NORMINV, NORMSDIST, NORMSINV, PEARSON, PHI, POISSON, POISSON.DIST, POISSONDIST, RSQ, SKEW, SKEW.P, SKEWP, SLOPE, SMALL, STANDARDIZE, STEYX, T.DIST, T.DIST.2T, T.DIST.RT, T.INV, T.INV.2T, T.TEST, TDIST, TDIST2T, TDISTRT, TINV, TINV2T, TTEST, WEIBULL, WEIBULL.DIST, WEIBULLDIST, Z.TEST, ZTEST
• Financial (28) — CUMIPMT, CUMPRINC, DB, DDB, DOLLARDE, DOLLARFR, EFFECT, FV, FVSCHEDULE, IPMT, IRR, ISPMT, MIRR, NOMINAL, NPER, NPV, PDURATION, PMT, PPMT, PV, RATE, RRI, SLN, SYD, TBILLEQ, TBILLPRICE, TBILLYIELD, XNPV
• Date & time (27) — DATE, DATEDIF, DATEVALUE, DAY, DAYS, DAYS360, EDATE, EOMONTH, HOUR, INTERVAL, ISOWEEKNUM, MINUTE, MONTH, NETWORKDAYS, NETWORKDAYS.INTL, NOW, SECOND, TEXT, TIME, TIMEVALUE, TODAY, WEEKDAY, WEEKNUM, WORKDAY, WORKDAY.INTL, YEAR, YEARFRAC
• Text (26) — CHAR, CLEAN, CODE, CONCATENATE, EXACT, FIND, FORMULATEXT, HYPERLINK, LEFT, LEN, LOWER, MID, N, PROPER, REPLACE, REPT, RIGHT, SEARCH, SPLIT, SUBSTITUTE, T, TRIM, UNICHAR, UNICODE, UPPER, VALUE
• Logical (12) — AND, CHOOSE, FALSE, IF, IFERROR, IFNA, IFS, NOT, OR, SWITCH, TRUE, XOR
• Lookup & reference (13) — ADDRESS, ARRAYFORMULA, ARRAY_CONSTRAIN, FILTER, HLOOKUP, MATCH, MAXPOOL, MEDIANPOOL, MMULT, OFFSET, TRANSPOSE, VLOOKUP, XLOOKUP
• Information (21) — COLUMN, COLUMNS, INDEX, ISBINARY, ISBLANK, ISERR, ISERROR, ISEVEN, ISFORMULA, ISLOGICAL, ISNA, ISNONTEXT, ISNUMBER, ISODD, ISREF, ISTEXT, NA, ROW, ROWS, SHEET, SHEETS
• Engineering (46) — BASE, BIN2DEC, BIN2HEX, BIN2OCT, BITAND, BITLSHIFT, BITOR, BITRSHIFT, BITXOR, COMPLEX, DEC2BIN, DEC2HEX, DEC2OCT, DECIMAL, DELTA, HEX2BIN, HEX2DEC, HEX2OCT, IMABS, IMAGINARY, IMARGUMENT, IMCONJUGATE, IMCOS, IMCOSH, IMCOT, IMCSC, IMCSCH, IMDIV, IMEXP, IMLN, IMLOG10, IMLOG2, IMPOWER, IMPRODUCT, IMREAL, IMSEC, IMSECH, IMSIN, IMSINH, IMSQRT, IMSUB, IMSUM, IMTAN, OCT2BIN, OCT2DEC, OCT2HEX

The engine has no INDIRECT, WEBSERVICE, RTD, DDE — there is no dynamic-reference, network, or external-data function in the set, so a recalc can't reach off-workbook. The absent functions are the sandbox boundary.

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FP&A workflows

xlsx-for-ai is built for agents working on real financial spreadsheets. Common workflows:

Budget vs. actual variance analysis

xlsx_read → extract actuals and budget → agent computes variances → xlsx_write → deliver updated workbook

Month-end reconciliation

xlsx_read (bank export) + xlsx_read (GL extract) → agent matches rows → xlsx_diff → audit trail of unmatched items

Audit-trail extraction

xlsx_schema → identify change-log columns → xlsx_read with sheet filter → agent summarizes changes by author/date

Multi-entity consolidation

xlsx_read × N entity files → agent aggregates → xlsx_write → consolidated workbook with intercompany eliminations noted

Pre-share PII redaction

xlsx_redact → strips SSNs, emails, employee IDs → redacted file safe for external distribution

These workflows are the reason tool descriptions are FP&A-legible: when a developer builds an agent for a finance team, the agent's LLM reads the tool descriptions and routes correctly without extra prompt engineering.

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Reliability features

• Deterministic diffs. xlsx_diff produces identical output for identical inputs — safe to version-control, safe to assert against in CI.
• Confidence-rated schema inference. xlsx_schema returns type confidence scores alongside inferred types. Agents can branch on confidence rather than trusting a blind guess.
• Audit trail. Every tool call — success or failure — is logged server-side with timestamp, client ID, endpoint, file size, latency, and error class.
• Hardened input validation. Four pre-engine guards on every uploaded buffer: billion-laughs XML bomb defense, control-character stripping, worksheet buffer ceiling (slow ZIP-bomb defense), and typed error chaining. Applied before the xlsx engine sees any bytes.
• Agent-readable errors. Rate-limit and validation errors return structured JSON — agents can read them and prompt the user intelligently, not just surface a status code.

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Privacy

Files are transmitted to https://api.xlsx-for-ai.dev over HTTPS and processed in memory. Files are not persisted beyond the duration of a single request. No email is collected. Registration is anonymous UUID only.

See PRIVACY.md for the full data-handling policy.

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What it costs

Free. All 50 tools, no paid tiers. No credit card, no email — registration is an anonymous client UUID created on first call. A volume cap (10,000 calls/month) keeps the hosted API healthy; that's the only limit.

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License

The npm client (xlsx-for-ai, this package) is MIT. The hosted API server (xlsx-for-ai-server) is proprietary — engine IP, rendering pipeline, and semantic-diff algorithm are not open source.

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Architecture

agent (Claude Code / Cursor / Continue / Zed / Windsurf / custom)
  └── MCP stdio
        └── xlsx-for-ai-mcp  (this package, ~200 lines)
              └── POST /api/v1/tools/<name>  →  api.xlsx-for-ai.dev
                    └── server-side engine (ExcelJS, formula eval, schema inference, redaction)

Requirements: Node.js 22+. 1.5.x line stays maintained on main for users who cannot upgrade.

---

Config

Stored at ~/.xlsx-for-ai/config.json. Created automatically on first run.

{
  "client_id": "<uuid>",
  "api_key": "<opaque>",
  "registered_at": "2026-05-05T00:00:00.000Z",
  "telemetry": false,
  "consent_version": 1
}

Telemetry is opt-in:

xlsx-for-ai --enable-telemetry
xlsx-for-ai --disable-telemetry
xlsx-for-ai --telemetry-status

Privacy modes — error-capture is off by default; when enabled, cell values are stripped before anything is retained (structure only, 30-day TTL, never used for training). XFA_PRIVACY=strict opts out entirely. See PRIVACY.md:

# Per-session flag (applies to all tool calls in the CLI invocation)
xlsx-for-ai --privacy=strict myfile.xlsx

# Environment variable (applies globally to all requests in the process)
XFA_PRIVACY=strict xlsx-for-ai myfile.xlsx

# In MCP server config (applies to all tool calls from the MCP server):
# Set XFA_PRIVACY=strict in your MCP server's env block

Delete the config to reset your client ID and API key:

rm ~/.xlsx-for-ai/config.json

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Migration from 1.5.x

The config file at ~/.xlsx-for-ai/config.json is extended in-place — existing telemetry consent is preserved.

---

Security

See SECURITY.md. All file content is transmitted to https://api.xlsx-for-ai.dev over HTTPS. Files are not retained beyond the duration of a single request.