blend-ai

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool sets a value and returns a confirmation, but doesn't cover critical aspects like whether this change is persistent, requires specific permissions, affects performance, or has side effects on rendering. For a mutation tool with zero annotation coverage, this is insufficient.

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 extremely concise and well-structured. It opens with the core purpose, then uses clear sections for 'Args' and 'Returns' with bullet-like formatting. Every sentence earns its place, and there's no wasted verbiage, making it easy to parse quickly.

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 (single parameter mutation), the description covers the basic purpose and parameter semantics adequately. However, with no annotations and an output schema present (though unspecified here), it lacks behavioral context and usage guidelines. It's minimally viable but has clear gaps in completeness for safe and 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?

The description adds significant value beyond the input schema, which has 0% description coverage. It explains that 'samples' is the 'Number of samples' with a 'Range: 1-10000,' providing essential context that the schema's bare integer type lacks. This compensates well for the schema's deficiency, though it doesn't detail units or typical values.

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: 'Set the number of render samples.' It specifies the verb ('Set') and the resource ('render samples'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'set_render_engine' or 'set_render_resolution', which prevents a perfect score.

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., whether a render engine must be active), exclusions, or related tools like 'render_image' or 'render_animation'. This lack of context leaves the agent guessing about appropriate usage scenarios.

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