csv-mcp

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validate_csv

Validate CSV data by applying column rules for types, required fields, uniqueness, and allowed values, detecting issues early.

Instructions

Validate columns, row widths, types, required values, uniqueness, and categories.

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": {
      "path": "<path>",
      "schema": "<schema>"
    },
    "name": "validate_csv"
  }
}

Input Schema

Name	Required	Description	Default
`path`	Yes
`schema`	Yes
`options`	No
`issue_limit`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Jul 29, 2026

TDQS

B3.1/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It fails to mention that the tool is read-only, whether it modifies data, or any side effects. The description only lists validation types but no behavioral traits.

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 a single concise sentence with no wasted words. It effectively lists the validation areas but could be improved by front-loading the primary action (e.g., 'Validate a CSV file against rules'). Still, it is appropriately sized.

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

Completeness3/5

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

Given the tool's complexity (4 parameters, nested objects, output schema exists), the description is somewhat incomplete. It does not mention the key 'schema' parameter that defines rules, nor the return format. However, the output schema likely covers return values, reducing the burden.

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

Parameters2/5

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

Schema description coverage is 0% for top-level parameters. The description adds no specific meaning beyond what the schema provides. It mentions validation types that relate to the 'schema' parameter but does not explain how parameters like 'path', 'options', or 'issue_limit' are used.

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 explicitly lists validation aspects (columns, row widths, types, required values, uniqueness, categories), clearly stating what the tool does. It distinguishes from sibling tools which handle other CSV operations like reading, querying, or cleaning.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives. The description does not mention prerequisites, scenarios, or when not to use it. The sibling names provide context but the description itself offers no usage direction.

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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