blend-ai

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

With no annotations provided, the description carries the full burden. It discloses that the tool creates a constraint (implying a write operation) and returns a dict with the constraint's details. However, it lacks information on permissions, side effects (e.g., impact on existing constraints), error conditions, or rate limits. The behavioral disclosure is basic but not misleading.

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 a clear purpose statement followed by 'Args:' and 'Returns:' sections. It is appropriately sized, though the constraint type list is lengthy. Every sentence adds value, but the list could be more concise (e.g., grouped categories).

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 complexity (4 parameters, 0% schema coverage, no annotations) and the presence of an output schema (implied by 'Returns'), the description is fairly complete. It explains parameters thoroughly and outlines the return value. However, it lacks usage context and behavioral details like error handling, leaving some gaps for a mutation tool.

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 fully. It does so by explaining all four parameters: 'object_name' (object or armature), 'bone_name' (empty for object-level), 'constraint_type' (with a comprehensive enum list), and 'properties' (optional dict with examples). This adds significant meaning beyond the bare 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: 'Add a constraint to an object or bone.' It specifies the verb ('Add') and resource ('constraint'), but does not explicitly differentiate from sibling tools like 'add_modifier' or 'add_geometry_node' beyond the resource type. The distinction is implied but not articulated.

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?

No explicit guidance on when to use this tool versus alternatives is provided. The description mentions 'object or bone' and lists constraint types, but does not explain scenarios where constraints are preferred over modifiers or other animation tools. Usage context is minimal, relying on the agent's prior knowledge.

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