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

Annotations already declare safe read-only, non-destructive behavior, and the description adds critical context: semantic equivalence is not guaranteed, phrasing or meaning may change, and results should be compared on representative tasks. This goes well beyond the structured safety hints.

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 purpose is front-loaded, but the meaning-change warning is repeated in three separate sentences ('semantic equivalence not guaranteed', 'Do not assume... preserves all nuances', 'may change phrasing or meaning'), which makes the description less tight than it could be.

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?

Complete for a one-parameter transformation tool with rich annotations and no output schema: it covers purpose, usage context, safety caveats, return behavior, and corrects the potentially misleading 'is-' name. Nothing essential is missing.

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 schema fully documents the single 'text' parameter with its max length. The description adds no format, syntax, or constraint details beyond what the schema already provides, so it sits at the baseline for high schema coverage.

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?

States a specific verb ('Compress') and resource ('prompt whitespace and filler'), and explicitly clarifies it is a transformation tool rather than a boolean validator, which distinguishes it from the many is-* sibling tools. The return type ('compacted prompt text') is also stated.

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?

Provides clear context: use before passing text to a model when token budget matters, and warns against assuming semantic preservation. It does not name specific alternative tools, but the usage scenario is well defined.

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