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

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds a behavioral trait not in the annotations — that the numbers are 'Live' — which tells the agent the data is current rather than cached. Where the returned data comes from or how fresh is still unspecified.

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 sentences with zero waste: the payload of metrics is front-loaded, then the usage condition. Nothing is padded or redundant.

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 no-argument, read-only statistics tool with no output schema, the description adequately enumerates what comes back and when to reach for it. It could add a note on the data source or snapshot period, but nothing essential to invoking it is missing.

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 takes zero parameters, so there are no parameter semantics to document; the baseline for a no-arg tool is 4. The description correctly implies no input is required by framing this as a whole-market snapshot.

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 resource (market statistics) and enumerates the actual metrics returned — listing counts, Verified Pro rates, median tutor prices. This is concrete rather than tautological, though it only implies sibling differentiation via 'as a whole' rather than naming the narrower tools like tdd_city_gaps or tdd_tutors.

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?

Explicitly says to use it 'for questions about the Dutch-learning market as a whole,' which gives a clear scope condition that separates it from per-city or per-tutor siblings. It stops short of naming those alternatives or stating when not to use it.

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