match_game_style

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

With no annotations, the description carries the full behavioral burden and does so well. It discloses that no external vision service is invoked, existing assets are not changed, the host interprets images, the tool validates/saves rather than scores beauty, and confidence below 0.6 preserves current defaults. These are meaningful behavioral constraints that an agent could not infer from the schema alone.

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 front-loaded with purpose and then walks through the workflow, which is appropriate for a two-step vision tool. It is dense but every sentence adds useful information. There is minor redundancy between 'current AI host's vision' and 'The host interprets images', and the run-on style could be tighter, but overall it is well-structured.

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?

For a complex, stateful, two-step tool with no annotations and no output schema, the description provides enough context to use it correctly. It explains what is returned (images and source_token), how to continue (call again with token and analysis), what the tool affects (future designs), what it does not affect (existing assets), and its safety profile. The remaining gap—analysis object shape—is deferred to the returned schema.

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 explain the four parameters, and it largely does. evidence_ids is tied to describe_project_assets, use_reference is tied to set_art_brief's saved image, source_token is explained as coming from the returned images, and analysis is described as using the returned schema. It does not provide detailed type/format expectations for analysis, but the semantic mapping is strong.

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 gives a specific verb and resource: recognizing the project's visual look from actual images using host vision. It also states the downstream effect of applying inferred palette, roughness, shape, density, and art notes to future designs. However, it does not explicitly distinguish this tool from sibling style tools such as get_project_style or set_project_style, leaving some ambiguity about how it differs from them.

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 clearly outlines a two-step workflow: first obtain evidence IDs from describe_project_assets or use_reference for set_art_brief's saved image, then call again with the returned source_token and analysis. It also states the confidence threshold below which defaults are kept. It does not explicitly say when to use this tool instead of get_project_style or set_project_style, so the alternative-tool guidance is incomplete.

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