validate_model_schema

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

With no annotations provided, the description carries the full burden. It discloses the specific validation checks performed, which is useful. However, it does not mention whether the tool is read-only, what happens on failure, or any side effects. This leaves some behavioral assumptions unstated.

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 concise and well-structured. It opens with a clear one-sentence summary, followed by a succinct bulleted list of checks. Every sentence adds value and there is no redundancy or filler.

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?

The description is largely complete given the tool's complexity: one parameter, an output schema, and no annotations. It covers the purpose and key checks, and the output schema handles return-value details. Missing only a few behavioral notes (e.g., non-destructive guarantee) and explicit usage guidance, which are already penalized in other dimensions.

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 has no description for the single parameter 'model_code', and schema coverage is 0%. The description adds that it is a 'Pydantic model definition', which clarifies the parameter's role, but it does not specify the exact format (e.g., source code string, file path, JSON). This partial compensation is insufficient to fully overcome the schema gap.

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 clearly states the tool's purpose: 'Validate a Pydantic model definition for WSQLite compatibility.' It uses a specific verb and resource, and the bulleted list of checks further specifies the scope. This clearly distinguishes it from sibling tools like generate_migration_from_models or deploy_wsqlite_scaffolding.

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 usage context: you would use this tool to validate a model before relying on it in WSQLite. However, it does not explicitly mention when to use it relative to siblings, nor does it state exclusions or alternatives. The guidance is implicit rather than explicit.

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