word_similarity

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

No annotations are provided, so the description carries the full burden. It does not disclose behavior like case sensitivity, handling of empty strings, or output format. The return value semantics are omitted.

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 concise, with a clear structure: purpose, usage note, then parameter list. It is front-loaded with the main purpose and avoids unnecessary words.

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 tool's simplicity, output schema existence, and no annotations, the description adequately covers the purpose, usage, and parameter semantics. It omits edge cases but is sufficient for a straightforward similarity 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 description adds meaningful explanations for each parameter beyond the schema, such as 'first string to compare' and lists method options with defaults. Despite the context indicating 0% schema coverage, the description actually provides parameter descriptions.

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 it calculates string similarity between two words or phrases and lists the algorithms. It distinguishes from siblings like levenshtein_distance by specifying different methods, but does not explicitly differentiate.

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 says 'Use this to compare strings using various algorithms,' which implies when to use it, but it does not specify when not to use it or mention alternative tools. The context from sibling tools is not leveraged.

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