blockbench_audit_model

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

No annotations are provided, so the description bears full responsibility. It lists what is checked but does not disclose whether the tool is read-only, whether it modifies the model, or what happens on failure. The behavioral implications are insufficiently clarified.

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 a single sentence listing 8 items, which is relatively concise and front-loaded with the verb 'Audit'. However, the long list with 'and' could be more structured, e.g., using bullet points, but it remains clear and efficient.

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 the complexity of the audit (many checks) and the lack of an output schema, the description is incomplete. It does not explain the output format, return values, or error handling, leaving the agent with insufficient information about what to expect.

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 0%, and the description does not mention the 'profile' parameter at all. It fails to explain the meaning or impact of the enum options (generic, weapon, pet, entity), leaving the agent without guidance on how to use the parameter.

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: auditing the active model for specific issues like invalid bounds, missing textures, etc. It uses a strong verb 'Audit' and lists concrete checks, distinguishing it from sibling tools like blockbench_quality_report.

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?

No guidance on when to use this tool versus alternatives, no prerequisites or context for usage. The description fails to indicate when auditing is appropriate or when other tools should be preferred.

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