# NetCafe Tables Muse connector

From musedirectory.ai, the independent directory of Meta Muse connectors. Not affiliated with Meta.

## NetCafe Tables

Convert messy spreadsheets into clean, verifiable tables with built-in arithmetic proof.

- Record: https://musedirectory.ai/connector/netcafe-tables
- Category: Finance & Bills
- Developer: ainetcafe (https://ainetcafe.com/duizhang)
- Muse status: Extra setup. Not in Muse's Connectors list yet. Muse can still use it: its page gives you a request to paste into Muse.
- Health: Working, 2947ms, checked 2026-09-28T08:15:49Z
- Endpoint: https://ainetcafe.com/mcp/table?s=registry
- Auth: No account needed; Pricing: unknown
- Screening: Screened, no issues found (2026-09-24T10:41:16Z)
- Source: Found in the official MCP Registry (com.ainetcafe/netcafe-tables) https://registry.modelcontextprotocol.io/v0/servers?search=com.ainetcafe%2Fnetcafe-tables

Cleans CSV and Excel exports, reconciles bank statements against ledgers, matches transactions by amount and date, deduplicates entities, and converts between CSV, JSON, Excel and accounting formats like QBO. Every result includes verification that row counts and totals are correct.

Example request: "Reconcile my bank statement against my accounting books to find missing or mismatched transactions."

How to connect: Not in Muse's Connectors list yet, but Muse can still use it. Paste this into Muse: "Use NetCafe Tables to help me. It is a free service with an MCP server at https://ainetcafe.com/mcp/table?s=registry. It does not need an API key. Ask me before you share anything with it." Muse asks before it shares anything with the app's site. Meta does not review apps used this way, so only use ones you trust. We tested this in the Muse app on September 24, 2026: Muse used an app's link directly this way and returned a live answer.

Tools:
- what_can_you_do: Describe a task in plain language (any language) and get back exactly which tools on this server do it, with ready-to-run example calls — instead of reading the whole catalogue and guessing. Also returns multi-step recipes when a task needs several tools chained (invoices to a le
- csv_to_qbo: Convert a transaction CSV into a .qbo / OFX bank-feed file that QuickBooks and similar accounting software import directly. Needs date, description and amount columns (or debit + credit). Pairs with extract_statement: statement PDF in, importable bank feed out.
- diff_tables: Matches rows across two CSVs on a key column and reports three things: keys only in A, keys only in B, and keys in both whose other columns disagree — naming the exact column and both values. Unlike reconcile_ledger this needs no amount column, so it also fits name lists, invento
- clean_table: Tidies a spreadsheet export: removes duplicate rows, trims whitespace (half-width and full-width — Chinese exports are full of ), unifies the half-dozen ways a cell can say "empty" (NA / null / - / 无), drops empty rows and columns, and can split one column into several. Returns t
- merge_tables: Combines up to 20 CSVs into a single table. Headers do not have to match: columns are unioned and a file missing a column contributes blanks for it, so rows never shift silently — the failure mode that makes hand-merged spreadsheets untrustworthy. Reports each source file row cou
- reconcile_ledger: Reconciles two sets of records — your books against a bank, platform, or supplier statement. Matches rows on a key column, compares an amount column, and returns three lists: only in A, only in B, and same key but different amount. Amounts are compared in integer cents, so 0.1 + 
- fix_csv_encoding: Detect the real encoding of a CSV (GB18030, Shift-JIS, Windows-1252…), repair mojibake (UTF-8 that was read as Latin-1, e.g. "é"), and re-emit UTF-8 with a BOM so Excel opens it correctly.
- read_xlsx: Read an Excel .xlsx workbook (by URL) into rows — every sheet, or one you name. Returns cell values (not formula text), dates as YYYY-MM-DD instead of Excel serial numbers, and keeps leading zeros so ID/postcode columns are not silently mangled. Says plainly which sheet it used, 
- write_xlsx: Build an Excel .xlsx file from rows (CSV text or JSON arrays), optionally several sheets at once. Numbers are written as real numbers so they sum in Excel, while values with leading zeros stay text so IDs and postcodes survive the round trip.
- match_transactions: Match bank statement lines to ledger/invoice entries when there is NO shared key — by amount, date window, reference numbers found inside free-text descriptions, and fuzzy counterparty names ("北京XX科技" vs "XX科技(北京)"). Handles split payments (one invoice paid in instalments, 1:N) a
- dedupe_entities: Find records in a supplier/customer/store list that are probably the SAME entity under different names — "北京星辰科技有限公司" vs "星辰科技(北京)" — by cross-checking name similarity against hard identifiers: tax ID (统一社会信用代码, checksum-verified), phone, domain, bank account, address. It never m
- csv_to_md_table: CSV (text or URL) → GitHub-flavoured Markdown table.
- csv_to_chart: CSV (first column = labels, second = values) → chart PNG in one call.
- csv_to_json: CSV (text or URL) → JSON array of objects (first row = keys). Returns a .json file.
- json_to_csv: JSON array of objects → CSV file. Flattens keys, quotes fields containing commas.

Screening checks:
- MCP handshake: pass (Answered in 410ms)
- Domain against threat feeds (Cloudflare security DNS): pass (ainetcafe.com not flagged)
- Published packages against the OSV malicious-package database: n/a (No npm or PyPI package published)
- Hidden instructions or invisible characters in tool text: pass (15 tools read, nothing found)
- Inputs asking for passwords, card numbers or seed phrases: pass (None found)
- Domain and redirects: pass (No redirects off the domain)
- AI review of purpose and tool behavior: pass (No concerns)