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

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations: it clarifies the transformation nature (input CSV → output JSON), specifies the output fields, and discloses the cost/payment requirement (0.02 USD per call via x402 on Base). This gives the agent important operational information not present in the structured fields.

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

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

The description is two sentences with zero fluff. The first sentence front-loads the core purpose, input, output, and downstream use; the second briefly states pricing and payment method. Every word earns its place, and the structure is easy to scan.

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

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

For a simple one-parameter tool without an output schema, the description is quite complete: it states the input type, output format, and output fields, and mentions the cost and payment method. The annotations cover the operational safety (read-only, idempotent). The only minor omission is the lack of an explicit example or error-handling details, but these are not critical for a straightforward transformation tool.

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

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

The schema already covers the 'csv' parameter with 'CSV con cabecera (máx. 1 MB)', but the description adds value by specifying that the CSV contains SaaS onboarding events and by describing the output properties (standard account IDs, event names, dates, plans). This helps the agent understand what input data is appropriate and what the output will look like, going beyond the schema's basic size/header note.

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

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

The description states a specific verb ('Convert'), the resource ('inconsistent SaaS onboarding event CSV files'), and the output ('clean JSON with standard account IDs, event names, dates, and plans for downstream email automation'). It clearly distinguishes itself from sibling tools like 'convierte-onboarding' and 'normalizar-requisitos-csv' by specifying the SaaS onboarding context and the email automation downstream use.

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

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

The description implies when to use the tool (when you have inconsistent SaaS onboarding event CSVs and need clean JSON for email workflows), but it does not explicitly state when not to use it or point to alternatives. Sibling names hint at alternatives, but the description itself offers no direct comparison or exclusion criteria.

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