llm_fs_find

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

No annotations are provided, so the description carries the full burden. It explains that the tool generates commands and that Claude executes them, adding behavioral context. However, it does not detail side effects, safety, or execution flow, leaving some ambiguity.

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 concise and well-structured: a clear purpose sentence followed by parameter definitions. Every sentence is necessary and no fluff. Ideal length for an AI agent.

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 tool's simplicity (2 params, no nested objects) and the presence of an output schema, the description is largely complete. It explains the purpose, parameters, and behavioral nuances. It could clarify whether the tool executes the search or just returns commands, but overall it's sufficient.

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 schema has 0% coverage for parameter descriptions, but the tool description explains both parameters thoroughly: description is illustrated with examples, and root is clarified with its default behavior. This adds significant value beyond the schema.

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 generates glob/grep commands to find files based on a natural-language description. It uses specific verbs and resources, distinguishing it from sibling tools like llm_fs_analyze_context or llm_fs_edit_many.

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 the tool routes to a cheap model for pattern generation and that Claude executes the commands, providing context for when it should be used. It does not explicitly exclude alternative tools, but the purpose is clearly distinct.

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