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

Beyond the annotations, the description discloses the return structure (verdict with pass/warn/fail, 0-100 score, per-check issue details) and variable costs per check type. It also highlights optional LLM-powered tone and factual analysis, giving the agent useful behavioral context. The description does not contradict any annotations.

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 three sentences, each packed with essential information: when to use, what it does, what it returns, a pointer to a related tool, and cost structure. It is front-loaded and contains no filler or redundant phrasing.

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 moderate complexity, the description covers the core purpose, usage window, return format, and cost, while the output schema handles verdict/score details. It omits discussion of the 'override' parameter and filter directives, but the schema covers those. The presence of output schema and annotations reduces the burden on the description, making it reasonably complete.

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 has 100% description coverage, so the baseline is 3. The description adds some context by grouping checks into deterministic vs LLM-powered and mentioning cost implications, but it does not elaborate on specific parameters like 'override' or 'directives'. The schema itself carries the semantic weight.

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 agent output against user-defined rules, using deterministic checks and optional LLM analysis. It explicitly distinguishes itself from related tools like qualitygate_trends, which is for spotting recurring failure patterns. The verb 'validate' plus the resource 'agent output' makes the purpose unmistakable.

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 provides clear timing guidance ('After your agent generates output, validate it ... before shipping') and an explicit alternative for trend analysis. However, it does not mention when *not* to use this tool or compare it with other validation-like sibling tools, so it lacks exclusionary guidance.

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