blender_ai_model_status

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

With no annotations, the description carries full responsibility for disclosing behavior. It mentions the import side effect, but does not explain polling semantics (e.g., returns immediately with status vs. blocks), what happens if the job is incomplete, whether it can be called repeatedly, or the format of the returned status. This is a significant gap for a status-check tool.

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, well-structured sentence that front-loads the primary purpose and mentions the optional import action. It is concise without being under-specified.

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 tool with no output schema, the description should explain what the status response contains and how to interpret it. It also lacks context about typical usage patterns (e.g., polling loop, expected frequency). The description covers the action but not the full operational context needed for an agent to use it correctly.

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% for both parameters. The description adds no extra meaning beyond what the schema already provides (job_id is from generation, auto_import controls import). Since the schema does the heavy lifting, a 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 states a specific verb ('check'), a clear resource ('status of an AI model generation job'), and an optional action ('import the completed model'). It clearly distinguishes from generation tools like blender_ai_generate_model and blender_ai_generate_model_sync by focusing on status polling and post-generation import.

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 explicit guidance is provided about when to use this tool versus alternatives. It does not mention that it should be used after blender_ai_generate_model for asynchronous jobs, nor does it contrast with synchronous generation. The relationship is implied by the parameter description but not stated.

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