blend-ai

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

No annotations are provided, so the description carries full burden. It states the tool performs a boolean operation and returns a confirmation dict, but lacks critical behavioral details: whether this modifies/destroys original objects, requires specific object types (e.g., meshes), has side effects, or handles errors. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

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 (Args, Returns) and uses minimal sentences. Each part earns its place by explaining parameters and return value. However, the opening sentence is somewhat redundant with the tool name, and it could be more front-loaded with key behavioral information.

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 3 parameters with 0% schema coverage and no annotations, the description does a decent job explaining parameters and mentions a return type. However, as a mutation tool in a 3D modeling context (Blender), it lacks crucial context: what happens to original objects, object type requirements, and error conditions. The output schema exists but isn't described here, leaving gaps in completeness.

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 successfully explains all three parameters: 'object_name' and 'target_name' as the objects involved, and 'operation' with its enum values (UNION, DIFFERENCE, INTERSECT). This adds essential meaning beyond the bare schema, though it doesn't detail object naming conventions or operation semantics.

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 performs a boolean operation between two objects, specifying the verb 'perform' and resources 'two objects'. It distinguishes from siblings by focusing on boolean operations rather than creation, modification, or deletion. However, it doesn't explicitly differentiate from similar tools like 'join_objects' or 'separate_mesh' which might handle object combinations differently.

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. It doesn't mention prerequisites (e.g., existing objects), exclusions, or compare it to sibling tools like 'join_objects' or 'separate_mesh' that might handle object relationships differently. Usage is implied through parameter names but not explicitly stated.

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