llm_quality_guard

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

With no annotations provided, the description carries full burden. It discloses that it computes rolling average scores and alerts for scores <0.7 with sufficient samples, but does not define 'sufficient samples', explain data source, or mention any side effects, rate limits, or performance impact.

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 extremely concise: two sentences plus structured Args/Returns section. It is front-loaded with the title line and contains no redundant information. Every sentence adds value.

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?

For a simple tool with one parameter and an output schema (not shown), the description covers the main functionality, return format, and parameter. It lacks details on the sample threshold for alerts and data freshness, but is otherwise complete for its complexity.

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?

Despite 0% schema description coverage, the description clearly explains the 'days' parameter: 'Number of days of history to analyze (default 7).' This adds meaning beyond the schema, specifying purpose and default value, which fully compensates for the lack of schema documentation.

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 shows quality scores per model with degradation alerts, using specific verbs like 'show' and 'displays', and specifies the resource (models) and scope (past N days). This distinguishes it from sibling tools like llm_quality_report or llm_analyze.

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 implies usage for monitoring model quality over time and alerts for degradation, but does not explicitly state when to use this tool versus alternatives like llm_analyze or llm_quality_report. No exclusions or conditions for non-use are given.

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