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omeryemba

mcp-hayabusa

by omeryemba

suggest_rule

Find existing Sigma rules most relevant to your detection query. Scores rules by term matches in title, tags, and description to return the best candidates.

Instructions

Suggest existing installed Sigma rules relevant to a free-text query.

Unlike get_hayabusa_rules (exact case-insensitive substring match, returns every match), this scores each rule by how many of query's terms it matches -- a title match outweighs a tags match, which outweighs a description-only match -- and returns only the top max_suggestions candidates, most relevant first. Use this for "is there already a rule for X" / "which existing rule is closest to Y"; it finds and ranks existing rules, it does not write or generate new ones.

Args: query: Free-text description of the detection you're looking for, e.g. "mimikatz credential dumping". Required, non-empty. technique_id: Optional ATT&CK technique ID (e.g. "T1003.001") to restrict candidates to rules already tagged with that technique before ranking. max_suggestions: Maximum number of ranked candidates to return (default 10). rules_dir: Optional path to a rules directory. Defaults to the "rules" directory next to the resolved hayabusa binary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
rules_dirNo
technique_idNo
max_suggestionsNo
Behavior5/5

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

The description details the scoring mechanism (title match > tags > description match), the return of only top max_suggestions candidates, and that it ranks existing rules without writing new ones. Even without annotations, the behavioral traits are well disclosed.

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 well-structured with a clear purpose first, then contrast, usage guidance, and parameter details. It is slightly verbose but every sentence adds value; minor trimming could improve conciseness.

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?

The description lacks details about the return value structure (e.g., list of rules with scores). Given no output schema, this gap reduces completeness. Otherwise, it covers inputs and behavior well.

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

Parameters5/5

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

Despite 0% schema description coverage, the description compensates with thorough inline parameter explanations, including default values, constraints (e.g., query non-empty), and optional parameters like technique_id and rules_dir.

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

Purpose5/5

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

The description clearly states the tool suggests existing installed Sigma rules relevant to a free-text query. It distinguishes the tool's purpose from get_hayabusa_rules by explaining different matching and ranking behaviors.

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

Usage Guidelines5/5

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

The description explicitly states when to use this tool ('is there already a rule for X' / 'which existing rule is closest to Y') and contrasts it with get_hayabusa_rules, which provides exact substring matching. It also clarifies that the tool does not write or generate new rules.

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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