BlenderMCP

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool as a polling API, specifies completion criteria for both modes (e.g., 'Done' or 'COMPLETED'), explains failure states, and advises when to proceed. It doesn't mention rate limits, authentication needs, or error handling details, but covers core operational behavior adequately.

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 well-structured with clear sections for two modes, each explaining parameters, returns, and usage. Every sentence adds value, such as defining completion criteria and polling behavior. It could be slightly more concise by avoiding minor repetition (e.g., 'This is a polling API' appears twice), but overall it's efficient and front-loaded with the core purpose.

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 tool's complexity (two operational modes with different parameters and statuses), no annotations, and no output schema, the description provides substantial context: purpose, parameters, return interpretation, and behavioral guidance. It lacks details on error responses, polling intervals, or exact output structure, but covers the essentials for effective use.

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%, so the description must fully compensate. It does so excellently: it explains that 'subscription_key' is 'given in the generate model step' for MAIN_SITE mode, and 'request_id' is similarly provided for FAL_AI mode. This clarifies the source and purpose of each parameter beyond their generic titles in the schema.

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: 'Check if the Hyper3D Rodin generation task is completed.' It specifies the exact resource (Hyper3D Rodin generation task) and verb (check status), distinguishing it from siblings like 'generate_hyper3d_model_via_images' or 'get_hyper3d_status' by focusing on polling job completion rather than initiating generation or general status checks.

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 provides explicit usage guidance: 'This is a polling API, so only proceed if the status are finally determined.' It distinguishes between two modes (MAIN_SITE and FAL_AI) with different parameters and status interpretations, offering clear alternatives within the tool itself. However, it doesn't explicitly contrast with sibling tools like 'get_hyper3d_status'.

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