findings_overview

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

With no annotations provided, the description carries the full burden and does so exceptionally. It discloses that severity is a string with a canonical order, that zero buckets are explicitly shown to avoid ambiguity, that unknown values are marked UNKNOWN, that the tool probes for field population, and that output is compact tables rather than raw JSON. This goes well beyond typical descriptions.

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 longer than a minimal one, but every sentence earns its place by explaining the rationale behind non-obvious behavior (e.g., explicit zero buckets, the probe before aggregation, output format). The structure is logical: purpose, usage, behavioral details, then args. Slightly verbose but not wasteful.

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 tool's complexity and the absence of annotations, the description thoroughly covers purpose, parameters, output behavior, edge cases (missing levels, unknown values), and relationship to sibling tools. The output format is described even though an output schema exists, which helps set expectations. Nothing critical is missing.

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 provides only types and defaults with 0% schema description coverage, so the description must compensate. It does: each parameter (hours, top_agents, top_titles) is given a clear meaning, unit, and purpose, and defaults are restated with context. This fully resolves what the parameters control.

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 opens with a specific verb and resource: 'Summarise wazuh-findings-v5-* by severity, agent, rule title and category.' It clearly distinguishes itself from the sibling tool 'search' by explaining it is a frozen query for 'the breakdown every report starts with' and that 'search still covers everything else.'

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 explicitly states when to use this tool: for the recurring aggregation that reports start with, and notes that 'search' covers everything else. This provides clear guidance on usage vs. alternatives, even naming the specific alternative and the scoping relationship between the two.

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