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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is fully covered. The description adds batch support and normalized ratio but does not discuss edge cases, normalization formula, or output structure. This matches the baseline for annotations providing the main behavioral context.

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 two sentences with the primary function front-loaded and use cases following. Every word earns its place; there is no redundant or promotional language.

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?

This is a straightforward calculation tool with an output schema and solid annotations. The description plus schema fully cover usage, including batch mode and case sensitivity. The only minor omission (explicit guidance that a/b vs batch are mutually exclusive) is already clear from schema descriptions like 'single-pair mode.'

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 input schema has 100% description coverage, with each parameter ('a', 'b', 'batch', 'case_insensitive') already explaining its role. The description adds no new parameter-level details, so it earns the baseline 3 for high schema coverage.

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 opens with a specific verb and resource: 'Compute the Levenshtein (edit) distance and normalized similarity ratio between two strings.' This clearly distinguishes it from sibling tools like embedding_similarity or vector_similarity by naming the exact algorithm and output type. The mention of batch comparison further clarifies scope.

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 lists concrete use cases ('fuzzy string matching, deduplication, and test result comparison'), providing clear context for when to apply the tool. However, it does not explicitly name alternative tools or exclude scenarios, so it stops short of a 5.

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