get_model_schema

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only explains the function and parameters, with no mention of side effects, error conditions, performance, or authorization needs. This leaves significant gaps for safe invocation.

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 reasonably concise, using an Args section with bullet points for scope options. Every sentence contributes information, though it could be slightly tighter. Structurally clear.

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?

Given that an output schema exists, the description does not need to explain return values. It adequately covers inputs. However, it lacks any mention of errors or expected behavior when the model is not found, leaving some contextual gaps.

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 0% description coverage, but the description compensates by providing clear semantics for both parameters: model_name as 'Name of the model' and scope with enumerated options and defaults. This adds value beyond the bare schema titles.

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 verb 'Get' and the resource 'schema for a specific Pydantic model', making the purpose unmistakable. It distinguishes itself from siblings like 'find_models' which locate models rather than retrieving their schema.

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 explains the scope parameter options but does not explicitly state when to use this tool versus alternatives like 'find_models' or 'trace_model_inheritance'. Usage is implied but lacks explicit guidance on exclusions or preferred contexts.

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