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

With no annotations, the description carries the full disclosure burden. It explains that the tool captures a baseline, scores renders, chooses direction, returns an implementation contract, and compares before/after for a report. It explicitly states the agent must apply fixes in the repository, which is a notable behavioral expectation. It does not mention permissions or side effects, but the core workflow behavior is well disclosed.

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 more verbose than the minimal example, but it is well-structured with 'First call' and 'Final call' phases and front-loads the primary use case. It is dense with necessary instructions; the repetition of 'Standout' is slightly redundant but does not obscure meaning. Every sentence contributes to understanding the workflow.

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 no output schema, the description explains key return artifacts: the exact implementation contract, a client-safe before-and-after report, and a share link. It covers both phases of the workflow, the required inputs for each, and the final outcome expectation. For a complex tool with 10 parameters and no formal output schema, this description is remarkably complete.

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 100%, so the baseline is 3. The description adds significant semantic value by mapping parameters to workflow phases: url/source/business for the first call, and after_url/after_source for proof mode. This explains relationships between parameters beyond the schema's individual field descriptions, though not every parameter is explicitly discussed.

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 identifies this as 'THE PRIMARY STANDOUT WORKFLOW' and states it is for making an AI-built website client-ready without choosing individual design tools. It distinguishes itself from sibling tools by framing a two-phase workflow (polish then prove) with explicit deliverables, so the agent knows exactly what the tool accomplishes.

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?

It provides explicit when-to-use guidance: 'Use this when a user wants an AI-built website made client-ready without choosing individual design tools.' It also instructs on first and final call usage, including which parameters to pass, and adds a clear directive: 'Do not stop after returning advice; the completed outcome is fix, rerender, prove.' This leaves no ambiguity about when to invoke this workflow versus alternatives.

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