manage_object_construction

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions AI sampling and validation, but does not explain side effects (e.g., whether it destroys previous scripts or objects), error handling, permissions, or data persistence beyond immediate save. The modification workflow is described only implicitly.

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 and structured with bullet points for operations and a note on sampling requirements. It front-loads the central purpose, but the bullet points could be tightened and merged for even greater brevity.

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 (12 parameters, multi-step operations), the description provides a high-level overview but lacks key details such as parameter interdependencies, expected output examples, error scenarios, and precise modification logic (e.g., how stored scripts are found). The existence of an output schema does not compensate for these 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?

With 12 parameters and 0% schema description coverage, the description should compensate by explaining parameter roles. It references 'description' as natural language input but does not detail other parameters like 'complexity', 'style_preset', or 'reference_objects'. The output schema exists, but parameter meaning remains largely unclear.

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 it enables AI-powered object construction and modification via sampling, listing three specific sub-operations (construct, construct_and_save, modify). This distinguishes it from sibling tools like 'construct_object' by specifying the unique sampling mechanism and the range of actions.

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 mentions the requirement for clients supporting MCP sampling and briefly outlines each operation's flow. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., 'construct_object' or 'generate_blender_script'), and does not provide when-not or condition-based usage instructions.

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