sumo_qa_load_approaches

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the tool is safe and idempotent. The description adds value by specifying that output is plain text, which helps set expectations. No contradictions with 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 redundancy. The verb is front-loaded, and every sentence adds value. The description is efficiently structured.

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 zero parameters, annotations, and no output schema, the description is quite complete. It explains what the tool returns and why it is used. A slight improvement could be to define 'canonical QA approaches' or mention related tools, but it's adequate for a simple retrieval tool.

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 tool has no parameters, and the input schema coverage is 100%. The description does not need to explain parameters, and its focus on the return value is appropriate. It adds meaning beyond the schema by explaining the purpose of the output.

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 that the tool returns canonical QA approaches as plain text. It uses a specific verb ('Return') and identifies the resource ('canonical QA approaches'). While it doesn't explicitly differentiate from siblings like sumo_qa_deciding_approach or other load tools, the purpose is unambiguous.

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 mentions that the host LLM picks which approach fits a piece of work, implying usage for selecting approaches. However, it lacks explicit guidance on when not to use this tool or alternatives (e.g., sumo_qa_deciding_approach). Usage is implied but not clearly delineated.

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