optimize_prompt

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions auto-detection capabilities and supports for custom platforms, but lacks details on rate limits, authentication needs, or what the optimization output looks like. It adequately describes the tool's function but misses deeper behavioral traits.

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 appropriately sized and front-loaded, starting with the core purpose and key features. Every sentence adds value: the first states the purpose, the second lists supported categories and platforms, and the third explains auto-detection, with no wasted words.

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 of a 5-parameter tool with no annotations and no output schema, the description is moderately complete. It covers the tool's purpose and key features but lacks details on output format, error handling, or optimization specifics, which could leave gaps for an AI agent.

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 schema already documents all parameters thoroughly. The description adds value by explaining auto-detection for category and platform, and listing example platforms, but doesn't provide additional semantics beyond what the schema offers, such as how optimization differs per mode.

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 verb ('optimize') and resource ('prompt') with specific scope ('for a specific AI platform'). It distinguishes from siblings by focusing on prompt optimization rather than listing categories, modes, or platforms, which are handled by other tools like list_categories, list_modes, and list_platforms.

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 clear context about when to use the tool (optimizing prompts for AI platforms) and mentions auto-detection of category and platform when omitted. However, it doesn't explicitly state when NOT to use it or name specific alternatives among sibling tools, such as using register_platform for custom platforms first.

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