# com.ainetcafe/netcafe-tables (remote · ainetcafe.com)

Messy spreadsheets in, clean checkable tables out. Every result carries its arithmetic proof.

- Trust score: 64/100 (medium)
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-20

## Components

- remote · `ainetcafe.com`: 64/100 (this document), [markdown](https://verifymcp.io/servers/com-ainetcafe-netcafe-tables/mcp-table.md), [page](https://verifymcp.io/servers/com-ainetcafe-netcafe-tables/mcp-table)

## Channel facts

- Endpoint: `https://ainetcafe.com/mcp/table?s=registry`
- Transports: `streamable-http`
- Auth: `none`
- Version: `1.0.0`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-20.

- **Endpoint Security**: 57/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - Authorisation not fully verified: no authorisation is required to call this server, and 8 tool(s) never declared a destructiveHint. The MCP spec treats an absent hint as destructive by default, so we cannot call this surface safe.
  - HTTPS is enforced; there's no plaintext access path.
  - HSTS check failed: the Strict-Transport-Security header is absent.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 67/100
  - AI-judged instruction clarity (good).
  - Context-footprint check failed: tool/resource definitions use about 2029 tokens (~135/item across 15 items; 15 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 17/100
  - Stability observed for 5 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
  - Structured output schemas are declared (47% of tools); any adoption earns full credit.
- **Capabilities**: 100/100
  - Implements a current MCP spec version (2026-07-28).

## Install

### Claude

```bash
claude mcp add --transport http com-ainetcafe-netcafe-tables https://ainetcafe.com/mcp/table?s=registry
```

### Codex

```toml
[mcp_servers.com-ainetcafe-netcafe-tables]
url = "https://ainetcafe.com/mcp/table?s=registry"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "com-ainetcafe-netcafe-tables": {
      "type": "remote",
      "url": "https://ainetcafe.com/mcp/table?s=registry",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add com-ainetcafe-netcafe-tables --url https://ainetcafe.com/mcp/table?s=registry --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  com-ainetcafe-netcafe-tables:
    url: "https://ainetcafe.com/mcp/table?s=registry"
```

### Other

```json
{
  "mcpServers": {
    "com-ainetcafe-netcafe-tables": {
      "type": "http",
      "url": "https://ainetcafe.com/mcp/table?s=registry"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-20 (score 64, +1)

No change was recorded against any check on this day. Stability & Change Management went from 13 to 17. That category is still filling its 30-day observation window: 4 days of observed history at the previous scan, 5 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-08-18 (score 63, +1)

No change was recorded against any check on this day. Stability & Change Management went from 7 to 10. That category is still filling its 30-day observation window: 2 days of observed history at the previous scan, 3 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-08-17 (score 62, 0)

- [security] The server rewrote its instructions, which are the text every model session reads
- [functional regression] Tool coverage: 64% → 47%
- [functional regression] Schema quality: 1536 → 2029
- [functional] New tool “dedupe_entities”
- [functional] New tool “match_transactions”
- [functional] New tool “read_xlsx”
- [functional] New tool “write_xlsx”

### 2026-08-16 (score 62, +1)

- [functional regression] Schema quality: 1377 → 1536
- [functional improvement] Stability: unverified → 0.03
- [functional] New tool “what_can_you_do”

### 2026-08-15 (score 61)

First indexed and scored.

## MCP tools (15)

### `what_can_you_do` (~159 tokens)

Find the right tool for a task

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 ledger, a bank statement reconciled, a messy CSV turned into a deliverable). Deterministic and free: it calls no model, costs nothing, and never runs out of quota. Call this FIRST when you are not sure what this server offers.

Input parameters:

- `task` (string, required): What you are trying to do, e.g. "reconcile a bank statement against my books" or "把一堆发票整理成能入账的表格"

### `csv_to_qbo` (~140 tokens)

Transaction CSV → QuickBooks .qbo bank feed file

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.

Input parameters:

- `account_id` (string): Your account number as the accounting software expects it.
- `bank_id` (string): Routing / bank identifier, if your import asks for one.
- `csv` (string): CSV content with a header row.
- `currency` (string): Three-letter currency code, default USD.
- `url` (string): Or a link to the CSV.

### `diff_tables` (~155 tokens)

Two tables → what differs (the VLOOKUP job, no amounts needed)

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, inventory counts, permission tables, and any "these two exports should match" check.

Input parameters:

- `key` (string, required): Column that identifies a row, e.g. id.
- `text_a` (string): Or the first CSV content directly.
- `text_b` (string): Or the second CSV content directly.
- `url_a` (string): Link to the first CSV.
- `url_b` (string): Link to the second CSV.

### `clean_table` (~308 tokens)

Messy CSV → tidy CSV, with a report of every change

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 the cleaned CSV plus exactly what changed: rows in, rows out, duplicates removed, cells trimmed per column. It can also transpose rows/columns and unpivot a wide table into a long one. The row arithmetic is verified in code — if in − removed ≠ out, the response says so instead of handing back a table nobody can check. Use when a CSV came out of Excel or an export and needs cleaning before analysis.

Input parameters:

- `keep` (string): For wide_to_long: comma-separated id columns to keep as-is. Defaults to the first column.
- `ops` (string): Comma-separated, default "dedupe,trim,drop_empty,unify_blank". Also available: split_column, transpose (swap rows/columns), wide_to_long (unpivot a wide table into the long format analysis tools expe…
- `split_by` (string): Separator to split on, default a single space.
- `split_column` (string): Column name to split (requires ops to include split_column).
- `text` (string): The CSV content itself. Provide this or url.
- `url` (string): Link to the CSV. Provide this or text.

### `merge_tables` (~134 tokens)

Several CSVs → one, columns unioned, row counts proven

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 count and checks in code that they sum to the merged total. Use for monthly exports, per-store sheets, or any set of files with the same subject but drifting headers.

Input parameters:

- `texts` (array): Or pass the CSV contents directly as an array.
- `urls` (string): Comma-separated CSV links, at least two.

### `reconcile_ledger` (~269 tokens)

Two tables → what does not match (the VLOOKUP job), with the arithmetic proof

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 + 0.2 never invents a phantom difference for someone to chase. The response also proves the result: the listed differences are re-added and must equal the gap between the two totals, checked in code. Use for month-end close, platform payouts vs orders, or any "these two numbers should agree and do not" problem. This is the job people do by hand with VLOOKUP or a groupby and then cannot prove they got right.

Input parameters:

- `amount` (string, required): Numeric column to compare, e.g. amount.
- `key` (string, required): Column name to match rows on, e.g. order_id.
- `text_a` (string): Or the CSV content of side A directly.
- `text_b` (string): Or the CSV content of side B directly.
- `url_a` (string): Link to side A (e.g. your books).
- `url_b` (string): Link to side B (e.g. the statement).

### `fix_csv_encoding` (~94 tokens)

Fix a CSV that opens garbled in Excel

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.

Input parameters:

- `text` (string): Or paste the CSV content directly.
- `url` (string): Public URL of the CSV.

### `read_xlsx` (~89 tokens)

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, which sheets are hidden, and where merged cells left blanks, instead of guessing for you.

### `write_xlsx` (~59 tokens)

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` (~98 tokens)

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) and combined payments (one transfer covering several invoices, N:1). Its rule is: never guess — a pair is only auto-match

### `dedupe_entities` (~101 tokens)

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 merges anything: it returns candidate groups with the evidence for each link, pairs that need human review, and — just as

### `csv_to_md_table` (~25 tokens)

csv to md table

CSV (text or URL) → GitHub-flavoured Markdown table.

### `csv_to_chart` (~27 tokens)

csv to chart

CSV (first column = labels, second = values) → chart PNG in one call.

### `csv_to_json` (~32 tokens)

csv to json

CSV (text or URL) → JSON array of objects (first row = keys). Returns a .json file.

### `json_to_csv` (~27 tokens)

json to csv

JSON array of objects → CSV file. Flattens keys, quotes fields containing commas.

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/com-ainetcafe-netcafe-tables/mcp-table#diagnostics

## Score history

- 2026-08-20: 64
- 2026-08-19: 63
- 2026-08-18: 63
- 2026-08-17: 62
- 2026-08-16: 62
- 2026-08-15: 61

## Links

- Remote endpoint: https://ainetcafe.com/mcp/table?s=registry
- Repository: https://github.com/mario03690/ai-netcafe
- Website: https://ainetcafe.com/duizhang
- Changelog RSS feed: https://verifymcp.io/servers/com-ainetcafe-netcafe-tables/mcp-table.xml
- Changelog JSON feed: https://verifymcp.io/servers/com-ainetcafe-netcafe-tables/mcp-table.json
- HTML version of this page: https://verifymcp.io/servers/com-ainetcafe-netcafe-tables/mcp-table