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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the description's job is to add operational context — and it does: per-call credit pricing, the fact that schema-invalid input is rejected before billing, and the output keys. That billing behavior is genuinely useful information not present in the annotations.

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?

Two tight sentences: what it computes first, then return shape and pricing. Nothing is wasted and the most decision-relevant content is front-loaded.

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?

With no output schema, the description supplies the return shape itself, plus pricing and input-validation behavior. It is essentially complete for a single-parameter read-only tool, with only sibling differentiation left implicit.

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?

There is a single parameter at 100% schema description coverage, where the schema already documents 'up to 64,000 characters.' The description restates the output keys rather than adding parameter meaning, so the baseline 3 applies.

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 names specific outputs (word/sentence/character counts, reading time, Flesch reading-ease) for a text-measurement resource, which lets an agent distinguish it from siblings like token-count, keywords, or sentiment. It also enumerates the exact return shape, removing ambiguity about what 'statistics' means here.

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?

Usage is implied by the metrics it returns, but there is no explicit 'use this when' or differentiation from near neighbors such as token-count or keywords. The pricing note aids decision-making but is not a usage guideline; the agent must infer selection criteria itself.

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