validate_rule

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

No annotations provided, but the description thoroughly covers behavioral traits: it checks specific quality smells (empty references, missing falsepositives, missing MITRE tag, vague condition), explains that target_backend is informational and linter is backend-agnostic, and that strict mode promotes warnings to errors. No side effects are relevant given read-only nature.

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 sentences, first clearly states purpose. Second provides detail on usage and parameters. Slightly verbose with line breaks but no redundant info. Well organized.

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 the presence of an output schema and the tool's complexity (validation with multiple checks), the description covers purpose, param semantics, and behavioral details adequately. No missing information for effective use.

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?

Schema description coverage is 0%, but the description compensates by explaining the purpose of target_backend (informational, backend-agnostic) and strict (promotes warnings to errors). yaml_content is implied as the sigma rule. Strong semantic value beyond schema.

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?

Clearly states the tool validates a sigma YAML rule for schema correctness, pySigma compatibility, and best-practices linting. The verb 'validate' and resource 'sigma rule' are explicit, and it distinguishes from sibling tools (convert_rule, draft_rule) by focusing on correctness checks.

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?

Explicitly describes when to use: when caller has a sigma rule and needs to know if it is parseable, spec compliant, and free of quality smells. Also explains parameter behavior (target_backend is informational, strict promotes warnings to errors) and implicitly contrasts with conversion/drafting siblings.

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