Import Check: Validate & Clean CSV

Clean selected CSV columns and validate the result

clean_csv

Apply only requested column renames and transformations, then validate the output against schema. Cleanup column names refer to the original headers; schema names refer to the renamed output. Transformations run in listed order. decimal_comma changes only plain numbers such as 12,50; ambiguous grouping stays unchanged. No records or columns are removed. Returned csv_text is UTF-8-compatible with CRLF record endings. One paid operation includes cleanup and validation. Spreadsheet-safe escaping is opt-in and may make numeric negative cells fail numeric validation; inspect the final report before import.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "csv_text": "<csv_text>",
      "schema": "<schema>",
      "cleanup": "<cleanup>"
    },
    "name": "clean_csv"
  }
}

Input Schema

Name	Required	Default
`schema`	Yes
`cleanup`	Yes
`csv_text`	Yes
`delimiter`	No	,
`request_id`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 30, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Adds substantial context beyond the annotations: no records or columns are removed, output is UTF-8 with CRLF endings, it is a single paid operation, and spreadsheet_safe may break numeric validation. The 'no columns removed' claim is consistent with destructiveHint=false. It stops short of describing failure behavior (e.g., what happens if validation fails).

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?

Six tight sentences, each carrying distinct information; the core operation is front-loaded in sentence one and the risk caveat is deferred to the end where it belongs. Slightly dense, but no sentence is filler.

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?

With no output schema, the description usefully characterizes the return (csv_text, UTF-8, CRLF) and notes a final report. It omits error/validation-failure behavior and does not cover two top-level parameters, but for a stateless transform tool the picture is nearly complete.

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

Parameters4/5

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

Top-level schema coverage is 0%, so the description must compensate. It does for the hardest semantics: 'Cleanup column names refer to the original headers; schema names refer to the renamed output' disambiguates the two objects, and the decimal_comma note clarifies that action. It says nothing about the delimiter or request_id parameters, leaving those to the enum/defaults.

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 opening sentence names a specific compound action — apply only the requested column renames/transformations, then validate the output against schema. This distinguishes it from the validate_csv sibling, which only validates. An agent can tell what this tool does and how it differs from its siblings without opening the schema.

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

Usage Guidelines3/5

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

The description says transformations run 'in listed order' and that cleanup names refer to original headers while schema names refer to renamed output, which implies correct usage. However, it never states when to prefer this tool over validate_csv or get_csv_usage, nor any prerequisite/exclusion conditions. Usage is implied rather than specified.

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

All Import Check: Validate & Clean CSV tools Inspect

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