get_jaccard_similarity

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

The description explains word-level set overlap and the output range, which adds behavioral context beyond the schema. However, it does not disclose tokenization details, case sensitivity, or edge cases (e.g., empty strings). Given no annotations, this is adequate but not comprehensive.

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 sentence plus a clarifying note, highly concise and front-loaded. Every word provides essential meaning with no redundancy.

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 simple similarity tool with two required parameters and an output schema, the description explains the output semantics (0-1 range) but lacks details on tokenization and edge cases. It is complete enough for basic use but could be improved with preprocessing notes.

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 should compensate. It only implies parameters are text inputs for similarity computation but does not explicitly describe expected format or pre-processing. This adds minimal value beyond the schema's type declarations.

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 tool computes Jaccard similarity at word level and interprets the output range (0=no overlap, 1=identical). This specific verb+resource description effectively distinguishes it from sibling tools like cosine similarity and edit distance.

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 does not provide any guidance on when to use this tool versus alternatives such as cosine similarity or edit distance. It lacks explicit context for when to choose Jaccard similarity over other text comparison tools.

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