Blender MCP Bridge

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 describes the core behavior (color extraction and 3D model creation) and output format (JSON with status, colors, model info), but lacks details on error handling, performance (e.g., processing time), side effects (e.g., file creation in Blender), or dependencies (e.g., Blender installation). It doesn't contradict annotations, but could be more comprehensive.

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 and front-loaded: the first sentence states the core purpose, followed by a process explanation, then a clear 'Args' and 'Returns' section. Every sentence adds value without redundancy, and the bullet-like formatting enhances readability while remaining concise.

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 moderate complexity (3 parameters, no annotations, but with an output schema), the description is fairly complete. It covers purpose, parameters, and return values, and the output schema reduces the need to detail JSON structure. However, it lacks context on integration with Blender (e.g., scene management) and error cases, leaving some 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?

Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics for all three parameters: 'image_data' (Base64-encoded, can include data URL), 'model_type' (shape options with default), and 'model_name' (naming with default). This goes beyond the schema's basic titles and types, providing practical usage context.

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 with specific verbs ('create a 3D model in Blender') and resources ('with colors extracted from an image'), distinguishing it from sibling tools like 'blender_exec' (generic execution) and 'get_blender_scene' (retrieval). It explains the two-step process: color extraction from image and 3D model creation with matching materials.

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 no guidance on when to use this tool versus alternatives like 'blender_exec' or other 3D modeling approaches. It mentions the tool's function but lacks context about prerequisites (e.g., Blender availability), use cases (e.g., prototyping, visualization), or limitations (e.g., image complexity).

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