brand_compile

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

No annotations are provided, so the description carries full burden. It discloses inputs, outputs (including conditional generation for Session 2+ data), and return values. However, it does not mention potential side effects like file overwriting or required permissions.

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 dense paragraph that conveys multiple pieces of information efficiently. It front-loads the main purpose but could benefit from slight structuring (e.g., bullet points for outputs). Nonetheless, no wasted sentences.

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 has no output schema, the description explains return values (token counts, clarification items, file list). It also covers conditional behavior based on session data. This is sufficiently complete for a complex tool with many outputs.

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 input schema has no parameters (0 params, 100% coverage). With no parameters to describe, the baseline is 4. The description does not add parameter info but does not need to.

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: generating multiple design artifacts (DTCG tokens, design-synthesis.json, DESIGN.md, brand runtime, interaction policy) from extracted brand data. It uses specific verbs and resource names, making it easily distinguishable from siblings like brand_compile_messaging.

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?

Explicitly states when to use the tool: 'Use after brand_extract_web, brand_extract_site, brand_extract_visual, or brand_extract_figma.' This provides clear context and prerequisites, and the sibling list implies alternatives for other scenarios.

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