fuzzy_search

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

Annotations already indicate read-only, non-destructive, idempotent behavior. The description adds that it uses Levenshtein distance, returns suggestions, file locations, and similarity scores, and requires a prior index. This provides useful context beyond the annotations without contradiction.

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 extremely concise with two sentences and a prerequisite note. Every sentence adds value: first sentence defines purpose and output, second sentence states when to use, and the prerequisite is clearly noted. No wasted 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 absence of an output schema, the description adequately explains the return values (suggestions, file locations, similarity scores), states the prerequisite, and clarifies the intended use case. This is sufficient for a fuzzy search tool with well-documented parameters.

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% coverage with descriptions for all five parameters. The description does not add additional meaning to the parameters beyond what the schema provides, so a baseline score of 3 is appropriate.

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 performs fuzzy name matching for functions, classes, and methods using Levenshtein distance, and returns suggestions, file locations, and similarity scores. This specificity and resource identification distinguish it from sibling tools like search_code or index_repository.

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 indicates when to use the tool ('when you don't know the exact name') and explicitly states the prerequisite (index_repository must be called first). However, it does not explicitly exclude cases where exact names are known or suggest alternative tools like search_code.

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