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

The description explains the transformation behavior and output (token savings) but does not disclose potential side effects, limitations, or statelessness. With no annotations provided, full behavioral burden falls on the description, which lacks these details.

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, focused sentence that efficiently conveys the core functionality without unnecessary 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?

For a simple one-parameter tool, the description captures the essential behavior and return value, but it does not specify the exact output structure (e.g., whether the optimized prompt is also returned). This is a minor gap given the lack of an output 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?

The single parameter 'prompt' is clearly described in the schema, and the tool description adds context about the optimization operations. Since schema coverage is 100%, the description provides marginal additional semantic value, warranting a baseline score of 3.

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 function: optimizing a prompt via specific transformations (removing filler words, compressing verbosity) and returning token savings. This distinguishes it from sibling tools like analyze_prompt and estimate_tokens, which serve different purposes.

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 implies usage for prompt optimization but does not explicitly state when to choose this over alternatives. No references to sibling tools or exclusion conditions are provided, leaving the agent to infer from the name.

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