Import Check: Validate & Clean CSV

Validate a CSV before importing it

validate_csv

Check a CSV against your explicit schema; return all issue counts and at most 200 detailed findings. Headers match exactly; required=false permits empty cells, not missing headers. Unique compares raw non-empty strings. Dates use YYYY-MM-DD, decimals use a dot without grouping, booleans use true/false. A valid=false report is a completed paid operation, not a tool failure. No values are changed; use clean_csv for explicit transformations. Validity covers only the supplied rules, not destination-system or business correctness.

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>"
    },
    "name": "validate_csv"
  }
}

Input Schema

Name	Required	Description	Default
`schema`	Yes
`csv_text`	Yes	CSV text including header. UTF-8, at most 1 MB; no file paths or URLs.
`delimiter`	No		,
`request_id`	No	Optional 8–128 character retry key. Reuse only for identical arguments; changed data requires a new key.

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 30, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Adds substantial context beyond the annotations: result truncation cap ('at most 200 detailed findings'), and critically the billing semantics ('a valid=false report is a completed paid operation, not a tool failure'), which prevents an agent from misreading a failed validation as an error. Also states the no-mutation guarantee and the limited scope of the validity verdict.

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?

Dense but front-loaded, with the core purpose first and constraints ordered by importance. Each clause carries information, though the run-on constraint list could be broken up for readability.

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

Completeness5/5

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

With no output schema, the description still characterizes the return value (issue counts plus capped detailed findings), the error/billing semantics, and the verdict's scope. Nothing an agent needs to call this correctly and interpret the result is missing.

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?

Schema coverage is 50%, and the description compensates by specifying value-level semantics the schema omits: exact header matching, required=false permitting empty cells but not missing headers, unique comparing raw non-empty strings, and date/decimal/boolean lexical formats. Delimiter and request_id are left to their schema descriptions.

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?

States a specific verb+resource ('Check a CSV against your explicit schema') and immediately distinguishes itself from the sibling clean_csv by scope of responsibility. An agent can tell what it does and what it does not do 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 Guidelines4/5

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

Explicitly routes the agent: 'use clean_csv for explicit transformations' and clarifies that this tool changes no values. It also bounds validity to supplied rules rather than destination/business correctness. Missing only explicit negative conditions (e.g. when not to use it at all).

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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