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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered. The description adds the genuinely useful constraint that answers are option ids, not free text, and that each question carries id, reason, and allowed options. It doesn't mention pagination or result size, but adds real context beyond 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 tight sentences that front-load what is returned, then the shape of each item, then the answer-format constraint. No filler.

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 single-optional-parameter read tool with a 100%-covered schema and annotations, this is adequate. However, there is no output schema, so the description could say more about the response shape (e.g., grouping or ordering), and it gives no guidance on the sibling tool it naturally pairs with.

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 description coverage is 100%, and the enum values are documented in the schema itself, so the baseline is 3. The description does not restate or expand on the lang parameter's behavior (English fallback for untranslated texts), which the schema already handles.

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?

States a specific verb (Returns) and resource (self-assessment questions on AI readiness) and even enumerates the four content areas (data, processes, people, compliance). This distinguishes it clearly from siblings like assess_ai_readiness, which scores rather than retrieves questions.

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 the retrieval use case but never states when to call this versus assess_ai_readiness or plan_ai_rollout. The pairing with assess_ai_readiness is inferable but not explicit, so usage is only implied.

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