get_doc

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

With no annotations, the description carries the burden. It discloses that parsing only works for Google/NumPy styles and defines the special 'module' name, but lacks details on failure modes or behavior for non-standard docstrings.

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?

Two short paragraphs with a clear purpose statement and an Args list. No filler, front-loaded with the main verb and resource.

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?

With an output schema present, return structure is covered. The description adequately covers parameters and core behavior; the only minor gap is handling of non-matching styles, but overall 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 input schema has 0% description coverage, and the description fully compensates by documenting both params, including the format for symbol names like 'Class.method' and the special 'module' value.

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 uses specific verbs 'Extract' and 'parse' with a clear resource (docstring) and output components (summary/params/returns/raises). It clearly distinguishes from sibling tools like get_function_body or list_declarations.

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?

No guidance on when to use this tool vs alternatives. The style condition ('when it follows Google or NumPy style') is about parsing behavior, not selection context, and no alternative tools are mentioned.

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