Analyze Text

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the specific list of counts returned and emphasizes the 'exact' nature, which is beyond annotations. It does not mention edge cases like encoding or how words/sentences are defined, but given the annotations cover safety and the tool is simple, this is sufficient. No contradictions with 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?

The description is exceptionally concise: two sentences with zero waste. The first sentence front-loads the purpose and output list; the second adds a usage rationale. The web link is extra but not distracting. Every sentence earns its place, and the structure is ideal for quick parsing by an agent.

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 one-parameter tool with no output schema and simple annotations, the description is largely complete. It lists all output types and provides a usage guideline. It does not describe the return format (e.g., JSON structure), but since there is no output schema, the agent must infer that from the listed counts. This is acceptable given the tool's simplicity, though a note about the return shape would improve completeness.

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 has zero description coverage for the 'text' parameter (only type: string). The description says 'for a text' but adds no meaning beyond what the schema already provides. It does not clarify encoding, length limits, or formatting expectations. Since schema coverage is 0%, the description should compensate, but it merely restates the parameter name. This is a notable gap.

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 states a specific verb ('Exact counts') and resource ('a text'), and enumerates the exact metrics (characters, words, lines, sentences, paragraphs, bytes). It is immediately clear what the tool does and distinguishes it from siblings like diff_text or slugify, which serve different purposes. The explicit list of outputs leaves no ambiguity about the tool's function.

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 provides a clear usage directive: 'Use this rather than estimating — counting long text is a classic model failure.' This tells the agent when to prefer this tool (exact counting over estimation). It does not explicitly name alternatives or when-not-to-use conditions, but the directive is specific enough for most contexts. The guidance is actionable and relevant.

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