ai-netcafe

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

clean_table

Read-onlyIdempotent

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.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "clean_table"
  }
}

Input Schema

Name	Required	Description
`ops`	No	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 expect).
`url`	No	Link to the CSV. Provide this or text.
`keep`	No	For wide_to_long: comma-separated id columns to keep as-is. Defaults to the first column.
`text`	No	The CSV content itself. Provide this or url.
`split_by`	No	Separator to split on, default a single space.
`split_column`	No	Column name to split (requires ops to include split_column).

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedAug 17, 2026

addedInput schema / properties / keep

Added value: +{
+  "description": "For wide_to_long: comma-separated id columns to keep as-is. Defaults to the first column.",
+  "type": "string"
+}

changedInput schema / properties / ops / description

Previous value: -"Comma-separated, default \"dedupe,trim,drop_empty,unify_blank\". Also available: split_column."New value: +"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 expect)."

AddedAug 14, 2026

TDQS

A4.3/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds substantial behavioral context: it returns a cleaned CSV plus a report of changes ('rows in, rows out, duplicates removed, cells trimmed per column'), and it verifies row arithmetic, explicitly stating it reports inconsistencies instead of returning an unchecked table. This goes well beyond the annotations, which only cover safety.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is detailed but every sentence serves a purpose: listing operations, describing the report, explaining verification, additional transformations, and the use case. It's front-loaded with the main function and not wasteful, though it is a bit long due to the tool's multiple capabilities.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, operations, return value, verification, and use case, which is complete for an agent to select and invoke the tool correctly. The output schema and annotations fill remaining gaps (exact return structure and safety profile). No significant missing context, though it doesn't address edge cases like conflicting parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all six parameters are already documented in the input schema, including valid ops values. The description reinforces the purpose of split_column and wide_to_long but doesn't add meaningfully new parameter-level semantics beyond what the schema provides, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Tidies a spreadsheet export' and enumerates specific operations (removes duplicate rows, trims whitespace, unifies empty values, splits columns, transposes, unpivots). It distinguishes itself from sibling tools like diff_tables or extract_tables by focusing on cleaning and transforming CSV data for analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a clear use case: 'Use when a CSV came out of Excel or an export and needs cleaning before analysis.' It also explains why wide_to_long is useful ('long format analysis tools expect'), but it doesn't explicitly mention when not to use the tool or name alternative tools for exclusions, so it's a 4 rather than a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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