hayabusa_pivot_keywords_list

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

With no annotations, the description provides some behavioral context: it returns a bounded keyword list per category and depends on a config file. However, it does not mention side effects (e.g., read-only, file locking, permissions), performance for large files, or error scenarios. Additional behavioral details beyond the output structure are missing.

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 one-sentence purpose, a return-format note, then a clear Args list. Every sentence adds value, and the key action is front-loaded. No redundant or irrelevant content.

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 no output schema and no annotations, the description explains the return format (dict of category->list) but not exact data types or categories. It omits edge cases, error handling, and performance notes. While serviceable for a simple listing tool, it lacks completeness for an agent to fully understand outputs and limitations.

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% description coverage, but the description fully explains each parameter: target (path to file or directory), min_level (optional with example values), and max_keywords (default 200). This adds rich semantics beyond the schema's type and title, making the parameters actionable for an agent.

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 extracts pivot keywords (users, computers, IPs, etc.) from .evtx files and returns a dict of category->keyword list. This distinguishes it from siblings like hayabusa_search (search events) and scan_evtx (generic scan). The verb 'Extract' and specific resource 'pivot keywords' make the purpose unambiguous.

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 explains what the tool does but does not explicitly state when to use it over alternatives. It implies usage for obtaining pivot keywords but lacks when-not or alternative tool guidance. Sibling tools are listed but not differentiated in usage context.

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