describe_model

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

No annotations are provided, so the description must disclose behavioral traits. It describes a read-only operation ('describe') but does not explicitly state idempotency, safety, error behavior, or auth requirements. The description adds minimal transparency beyond stating the function.

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?

Two sentences: first states the function and outputs, second provides usage context. No redundant words, front-loaded with key information.

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 covers what the tool does and when to use it. It does not describe error handling (e.g., if model not found) or return format details, but given the simple input schema and no output schema, it is largely sufficient.

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 coverage is 100% with one parameter described. The description adds value by specifying that the model name comes from 'list_models', aiding proper use. However, it does not elaborate on format or constraints, so it slightly exceeds the baseline of 3.

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 specific verbs ('Describe one content model's field schema') and lists concrete attributes (names, types, required, etc.). It distinguishes itself from sibling tools like get_item (data) and list_models (model list) by focusing on the schema structure.

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 advises when to use this tool ('Use this to understand the exact shape of the data... instead of fetching the whole API or docs.') and hints at a prerequisite via 'Model name (from list_models)' in the parameter, though it does not mention when not to use it.

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