get_normalized_edit_distance

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

No annotations provided, so description carries full burden. It clarifies output scale but omits algorithm (e.g., Levenshtein), case sensitivity, or performance characteristics. Basic transparency but lacks depth.

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?

Two sentences, highly concise. Front-loads the key information about normalization and scale. Every sentence is informative and non-redundant.

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 output schema exists and tool is simple, description covers core behavior adequately. However, missing normalization method or algorithm details slightly reduces completeness for a distance metric 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?

Input schema has 0% description coverage, and description adds no extra meaning beyond parameter names and types. No details on encoding, length limits, or edge cases like empty strings.

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?

Description clearly states it computes normalized edit distance on a 0-1 scale, with explicit interpretation. Distinguishes from sibling 'get_edit_distance' by specifying normalization and scale.

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?

Description implies use for normalized similarity but provides no explicit when-to-use, when-not-to-use, or alternatives. Usage context is inferred rather than stated.

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