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

Annotations already indicate this is a safe, idempotent, read-only operation. The description adds valuable context by guaranteeing deterministic output and noting no API key is required, which helps agents assess reliability and dependencies. It also discloses the return format (optimized text with token savings breakdown), enhancing behavioral understanding.

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 concise and front-loaded with the primary action. Every sentence adds relevant detail, including deterministic behavior and the absence of an API key requirement. There is no redundant or unnecessary text.

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 presence of an output schema, comprehensive annotations, and the tool's straightforward nature, the description covers all essential context for an agent to use it correctly. It could optionally mention alternative tools, but the description is otherwise 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?

The input schema provides full documentation for both parameters (text and options), including defaults and descriptions. The tool description does not add significant parameter-level detail beyond what the schema already covers, but since schema coverage is 100%, the baseline of 3 is appropriate.

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 accurately states the tool's purpose with a specific verb ('Compress') and resource ('LLM prompt'), and clearly lists the compression techniques (removing filler words, verbose phrases, duplicate sentences, whitespace). It implicitly distinguishes from token counting or truncation tools, but does not explicitly name alternatives.

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 by stating the function ('Compress an LLM prompt'), but provides no explicit guidance on when to choose this tool over related siblings like count_tokens or truncate_to_tokens. There are no exclusions or alternative recommendations, so the usage context is only implied.

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