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 conversion action but doesn't explain what Train-Case entails (e.g., capitalization rules, handling of spaces/special characters), potential side effects, or output format. This leaves significant gaps for a tool with multiple parameters.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.

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 (4 parameters with 0% schema coverage, no annotations, no output schema), the description is incomplete. It doesn't explain the transformation behavior, parameter roles, or output format, which are essential for proper tool invocation. This is inadequate for a parameter-rich text processing 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?

Schema description coverage is 0%, meaning none of the 4 parameters (text, delimiter, locale, mergeAmbiguousCharacters) are documented in the schema. The description adds no information about these parameters, failing to compensate for the coverage gap. For example, it doesn't explain what 'delimiter' or 'locale' do in the context of Train-Case conversion.

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 clearly states the verb 'convert' and the resource 'text to Train-Case', making the purpose immediately understandable. It distinguishes from siblings by specifying the exact case transformation (Train-Case), though it doesn't explicitly contrast with other case conversion tools like case_to_camel or case_to_snake.

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?

No guidance is provided on when to use this tool versus alternatives. With many sibling tools for case conversion (e.g., case_to_camel, case_to_snake), the description lacks context on Train-Case's specific use cases or differences from other formats, leaving the agent to infer based on the name alone.

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