get_schema

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

Annotations already mark readOnlyHint=true, and the description adds value by noting 'Free' (likely cost-related) and summarizing the return contents (names, types, required, defaults). It doesn't contradict annotations and provides useful behavioral context beyond the structured metadata.

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?

Three concise sentences deliver purpose, usage, and cost without waste. The first sentence is front-loaded and informative, and each subsequent element (usage and cost) earns its place.

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 read-only tool with one well-documented parameter and an output schema (indicated by context), the description covers purpose, usage, cost, and a summary of the returned content. It is complete for an agent to decide to invoke the 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?

Schema description coverage is 100%; the model parameter is already described as 'Model id from list_models.' The description adds no extra parameter semantics, so the baseline score of 3 is appropriate.

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 uses a specific verb 'Get' and resource 'a model's input fields', clearly distinguishing it from sibling tools like list_models (lists models) and generate (produces output). It explicitly states 'Use before generate to know what to send.'

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 explicitly instructs 'Use before generate to know what to send', providing a clear workflow tie-in. It also references list_models in the parameter description, indicating the prerequisite step. This gives clear context for when to use the tool.

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