levenshtein_distance

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

With no annotations, the description carries the behavioral burden. It adds the useful trait that matching is deterministic and exact, but it does not disclose return format, similarity-ratio range, or case-sensitivity/normalization behavior. This is a partial disclosure, not a full one.

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?

One concise sentence, no filler, and the primary action is front-loaded. It earns its place despite being terse.

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?

For a two-string pure function, the core input and operation are clear, but there is no output schema and no description of what the computed result looks like. An agent could call it, but may not know how to interpret the similarity ratio or exact distance return value. This is adequate with visible gaps.

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%, so the description must compensate. It only says 'two strings,' which restates the type in the schema; it does not explain what a or b represent, length constraints, or expected encoding/format. The description adds minimal parameter meaning.

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 names a concrete operation: compute exact edit distance and similarity ratio between two strings. It is clear and uses the tool name meaningfully, but it does not explicitly distinguish this from sibling tools like diff_text.

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 given about when to choose this over diff_text or other string utilities, nor any exclusions or prerequisites. The 'deterministic fuzzy matching' phrase hints at a use case, but the agent is left to infer it.

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