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scan

Scan any directory for LLM API calls and receive a monthly cost estimate derived from token usage and pricing.

Instructions

Scan a directory for LLM API calls and estimate monthly costs.

Finds all LLM API call sites (OpenAI, Anthropic, etc.) in the given path and produces a cost estimate based on token counts and pricing.

Args: path: Directory or file path to scan. Defaults to current directory. calls_per_month: Assumed monthly call volume per call site. If not provided, the CLI default (1000) is used.

Returns: JSON string with the scan results including call sites and cost estimates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNo.
calls_per_monthNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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 details the scanning action, cost estimation, and return format. While it doesn't cover every edge case (e.g., recursion depth or error handling), it provides sufficient behavioral insight for a read-only analysis tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: a lead sentence, then details in Args and Returns sections. Every sentence adds value, and the format is easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (2 optional params, no annotations), the description covers the core behavior and return type adequately. It could mention recursion or failure modes, but it is sufficient for most use cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, but the description fully explains both parameters: 'path' (directory/file, default current dir) and 'calls_per_month' (monthly volume, default null implying CLI default of 1000). This adds essential meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool scans a directory for LLM API calls and estimates costs, specifying providers and purpose. This is a specific verb+resource that distinguishes it from the sibling 'diff'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly indicates when to use the tool (scanning directories for LLM calls and cost estimation). However, it does not explicitly mention when not to use it or provide alternatives, which prevents a top score.

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

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