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

Annotations cover read-only, idempotent, non-destructive behavior, so the description only needs to add algorithmic detail. It adds meaningful behavior by listing exactly which counts are computed (with/without spaces, words, SMS, Twitter remaining), reducing ambiguity about what 'count characters' means.

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?

One information-dense sentence presents the action and all output variants, followed by a short source attribution. No filler or repeated schema information.

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 a single string input, a full output schema, and annotations covering side effects, the description gives the agent everything needed to invoke and interpret results. Listing the concrete output categories is sufficient for this pure calculation tool.

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 already describes the only parameter ('The text to measure') at 100% coverage, so the description does not need to compensate. It only restates the same concept as 'a block of text' and adds no format, encoding, or length constraints beyond the schema.

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 starts with the concrete verb 'Count the characters in a block of text' and then names every output metric: spaces-removed count, word count, SMS segment count, and Twitter/X character remaining. This makes the tool easy to distinguish from sibling text tools like word_counter or text_splitter.

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 use case is clear from the description: any time an agent needs character-level measurements of a text block. It does not explicitly say when to prefer the sibling word_counter or text_splitter, but the scope and metrics imply the appropriate context without ambiguity.

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