count_words

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

No annotations are provided, so the description must fully disclose behavior. It lists what is counted but not how counts are performed (e.g., whitespace tokenization, whether characters include spaces, or reading speed assumption). This leaves ambiguity about the exact output.

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 a single sentence that is front-loaded with the primary action and lists all output categories. It is free of unnecessary words or repetition, making it maximally concise.

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 and no annotations, the description should indicate what the tool returns (e.g., a JSON object with word_count, char_count, etc.) and any relevant counting rules. It does not, leaving the agent without critical information for interpreting results.

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 only parameter 'text' is fully documented in the schema as 'Text to analyze', giving 100% coverage. The description reiterates the tool's purpose but adds no additional input-format constraints, so baseline 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 is clear and specific: 'Count words, characters, sentences, and estimate reading time' names the exact resources and actions. It distinguishes itself from sibling tools like slugify or convert_case, which perform transformations rather than analyses.

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?

There is no guidance on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or context where another sibling tool (e.g., extract_from_text) might be more suitable.

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