sumo_qa_load_standards

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral context: the filter is metadata-based (frontmatter) and returns plain text, which is not in 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?

Two concise sentences with no fluff. The most important information (what it returns and the filter behavior) is front-loaded.

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?

The description says 'as plain text' but doesn't specify the exact format or if there are size limits. While readOnlyHint ensures safety, more detail on the output structure would improve completeness.

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?

With 0% schema description coverage, the description fully explains the classification parameter: it accepts comma-separated values, is metadata-based, and does no keyword inference, which is essential for correct usage.

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 uses specific verb 'Return' and noun 'loaded standards packs as plain text', clearly distinguishing from sibling tools like sumo_qa_load_approaches or sumo_qa_load_classifications which load different content types.

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 explains the optional classification filter and explicitly states 'no keyword inference', setting clear expectations. While it doesn't explicitly contrast with alternatives, the tool name and context make the use case obvious.

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