validate_croissant

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

With no annotations present, the description carries the full behavioral burden and it succeeds: it discloses delegation to the official MLCommons validator, the specific checks performed, support for the GeoCroissant extension, and the exact shape of the returned dictionary including errors vs. warnings. No behavior is hidden.

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 structure is logical and front-loaded: purpose, validation steps, usage timing, supported variants, then return values as a bulleted list. It is thorough but not padded; the only minor redundancy is the validation-steps list being slightly over-explained, which prevents a 5.

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 validation tool with three well-documented inputs and a documented output dictionary, the description is complete: it covers when to call, what is validated, which document variants are supported, and what will be returned. The provided output schema further reduces the need for the description to explain return values.

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 input schema has 100% description coverage with clear per-parameter explanations for jsonld_url, jsonld_path, and jsonld_content. The tool description adds no additional parameter-level meaning, such as interaction between them or whether exactly one must be provided, so it stays at the high-coverage baseline.

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 opens with the specific verb+resource combination 'Validates a Croissant or GeoCroissant JSON-LD document' and enumerates the full validation pipeline (JSON syntax, JSON-LD expansion, structure-graph construction, schema conformance). This clearly distinguishes the tool from the sibling creation, search, and inspection tools.

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 gives explicit usage triggers: call it whenever the dataset description is created or edited, BEFORE publishing, and after any modification of an existing file. It does not name when-not-to-use cases or explicitly route to an alternative tool, which keeps it short of a full usage-and-exclusion statement.

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