Text-Toolkit

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('Calculate') but doesn't describe what the tool returns (e.g., scores, formats), whether it has side effects, performance characteristics, or error handling. For a tool with no annotation coverage, this is a significant gap in transparency.

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 extremely concise with just three words, front-loading the core purpose without any fluff. Every word earns its place by directly stating the tool's function, making it efficient and easy to parse.

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?

Given the complexity (a calculation tool with no annotations, no output schema, and low schema coverage), the description is incomplete. It doesn't explain what readability metrics are, how results are returned, or usage context, leaving the agent with insufficient information to effectively invoke the tool beyond basic parameter passing.

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 description adds no parameter semantics beyond the input schema. With 0% schema description coverage and 1 parameter ('text'), the schema alone defines it as a required string. The description doesn't explain what 'text' should contain (e.g., plain text, length limits, language), so it doesn't compensate for the low coverage, but the single parameter keeps it from being worse.

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 'Calculate readability metrics' states a clear verb ('Calculate') and resource ('readability metrics'), but it's somewhat vague about what specific metrics are calculated. It distinguishes from siblings like count_words or count_characters by focusing on readability rather than basic counts, but lacks specificity about what constitutes 'readability metrics' (e.g., Flesch-Kincaid, Gunning Fog).

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for use, or differentiate from sibling tools like count_words or string_analysis tools that might overlap in text processing. This leaves the agent without explicit direction on appropriate scenarios.

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