generate_scene

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 generates a scene but doesn't clarify whether this is a read-only operation, if it modifies existing data, what permissions are required, or the output format. For a tool with 6 parameters and no annotations, this leaves significant behavioral gaps, such as whether it creates persistent data or returns a temporary design.

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 a single, efficient sentence that front-loads the core purpose without unnecessary words. It directly states what the tool does ('Generate a lighting scene') and the key inputs ('based on script context and design preferences'), making it easy to parse. Every part of the sentence contributes to understanding, with no redundancy or fluff.

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 (6 parameters, nested objects, no output schema, and no annotations), the description is incomplete. It doesn't address behavioral aspects like mutation effects, error conditions, or output format, and it lacks usage guidelines. While concise, it fails to provide sufficient context for an AI agent to confidently invoke this tool without relying heavily on the schema, which has gaps in coverage.

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 67%, with parameters like 'sceneDescription' and 'projectId' well-documented in the schema. The description adds minimal value beyond the schema, mentioning 'script context and design preferences' which map to parameters but without extra details. It doesn't explain interactions between parameters (e.g., how 'sceneType' affects generation) or compensate for the 33% coverage gap, so it meets the baseline for adequate but not enhanced 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's purpose: 'Generate a lighting scene based on script context and design preferences.' It specifies the verb ('Generate') and resource ('lighting scene'), and distinguishes it from siblings like 'optimize_scene' or 'update_scene' by focusing on creation rather than modification. However, it doesn't explicitly differentiate from 'create_cue_sequence' or 'generate_act_cues', which are also generation tools, preventing 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., needing a project created first), compare it to siblings like 'optimize_scene' (for refining) or 'create_cue_sequence' (for sequential cues), or specify scenarios where it's appropriate. Usage is implied only through the tool name and parameters, lacking explicit context.

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