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list_directory

View directory contents to analyze Chef cookbooks, resources, and configuration files during migration to Ansible infrastructure.

Instructions

List the contents of a directory.

Args: path: The path to the directory to list.

Returns: A list of filenames in the directory, or an error message.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions returning 'A list of filenames in the directory, or an error message,' which covers basic output behavior. However, it lacks details on permissions required, error conditions (e.g., invalid paths), performance aspects, or whether it's read-only (implied but not stated). For a tool with no annotations, this is a significant gap in transparency.

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

Conciseness4/5

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

The description is appropriately sized and front-loaded, with the core purpose stated first ('List the contents of a directory.'), followed by brief sections for Args and Returns. It avoids unnecessary details, though the structure could be more integrated (e.g., combining purpose and parameters). Overall, it's efficient with minimal waste.

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

Completeness3/5

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

Given the tool's low complexity (one parameter) and the presence of an output schema (which likely covers return values), the description is somewhat complete. It explains the basic operation and output, but gaps remain in usage guidelines and behavioral details. Without annotations, it doesn't fully address the context, making it adequate but with clear room for improvement.

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

Parameters3/5

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

The description adds minimal semantics beyond the input schema. It explains that 'path' is 'The path to the directory to list,' which clarifies the parameter's purpose but doesn't provide format details (e.g., absolute vs. relative paths) or constraints. With 0% schema description coverage and only one parameter, the description compensates slightly but not fully, meeting the baseline for low coverage.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'List the contents of a directory.' It specifies the verb ('List') and resource ('contents of a directory'), making it easy to understand what the tool does. However, it doesn't differentiate from sibling tools like 'list_cookbook_structure' or 'read_file', which might have overlapping functionality in this context.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. With siblings like 'list_cookbook_structure' and 'read_file' that might handle similar tasks (e.g., listing files or reading directories), there's no explicit or implied context for choosing this tool over others. This lack of differentiation leaves usage unclear.

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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