validate_rule

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

No annotations provided, so description carries full burden. It describes specific checks (empty references, missing falsepositives, missing MITRE tag, vague condition). It also notes strict mode behavior and that target_backend is informational. No side effects mentioned but no destructive hints either.

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?

Concise at ~80 words, front-loaded with purpose. Every sentence adds value. No repetition or fluff.

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 output schema exists (not shown), return values need not be explained. Description covers validation specifics, sibling differentiation, and parameter roles. Could briefly mention that it returns a list of issues, but output schema likely covers that.

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%, so description compensates. It explains yaml_content is the rule to validate, target_backend is informational, and strict promotes warnings to errors. Adds meaningful context beyond bare parameter names.

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 validates sigma YAML rules for schema correctness, pySigma compatibility, and best-practices linting. It distinguishes from siblings (convert_rule, draft_rule) by focusing on validation.

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?

Explicit use cases are given (rule drafted, pasted, or read from disk). It mentions when to use it: to check parseability, spec compliance, and quality. Does not explicitly state when not to use it but provides clear context.

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