scan

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

Given no annotations, the description carries the full burden. It explains the scanning behavior, cost estimation based on token counts and pricing, and mentions default assumptions for calls_per_month. It does not explicitly state read-only nature or permissions, but is reasonably transparent for the tool's complexity.

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, front-loading the main action and output. It includes Args and Returns sections without extraneous information, earning its sentences.

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?

Despite no annotations, the description covers purpose, parameters, and return format (JSON string). With an output schema present, the return description suffices. The tool is simple with two optional parameters, and the description is complete for its scope.

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 provides zero description coverage, so the description adds significant value by explaining both parameters: 'path' (directory/file path, defaults to current) and 'calls_per_month' (assumed monthly volume, default 1000). This enables correct invocation.

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's purpose: scanning a directory for LLM API calls and estimating monthly costs. It explicitly mentions supported providers (OpenAI, Anthropic) and the output, distinguishing it from the sibling tool 'diff'.

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 implies usage for cost estimation but does not provide explicit guidance on when to use this tool versus alternatives. No when-not-to-use or alternative tools are mentioned, which leaves the agent to infer the context.

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