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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description adds real value beyond that: it discloses the return categories and the implicit routing/dispatch behavior for free-form questions. It stops short of describing result shape or size, but an output schema exists to carry that.

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 guidance sentences are front-loaded and purposeful, but the 19-item topic list duplicates the enum in the input schema verbatim, consuming most of the description's length without adding information. Trimming it would let the genuinely useful routing guidance stand out.

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?

Annotations and an output schema mean the description needn't explain safety or return values, and it covers the main call patterns. However, the free-text-question promise conflicts with the strict enum, leaving an agent uncertain whether a natural-language topic will be accepted or rejected at validation.

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?

Schema coverage is 100% with an enum and its own description, so the schema already does the heavy lifting. The description adds the omit-to-list-all behavior, which is useful, but it also promises that free-text questions like "how do I test my RAG" resolve to a topic — something the enum constraint does not permit, creating ambiguity about what values are actually legal.

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?

Opens with a specific verb+resource ("Query the IA-QA methodology knowledge base") and enumerates what comes back: guidelines, assertion strategies, thresholds, best practices, and related MCP tools. This is clearly distinguishable from data-mutating siblings and from single-purpose guides like multimodal_eval_guide.

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?

Gives explicit call patterns: omit the topic to list everything, use "start-here" when unsure where to begin, and plain questions resolve to a topic. It also states the intent — mapping what you are testing to the tools to call and the CI gate field — so an agent knows exactly when to reach for it versus a direct tool.

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