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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and a closed world, so the safety profile is covered. The description usefully adds that a snapshot is returned and that slim=true degrades the payload to counts and types, but says nothing about size, freshness or permissions.

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 with zero filler; the core payload description comes first, the slim mode second, and the usage constraint last. Every clause earns its place.

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?

With no output schema, the description does some of the work by naming the entity types and hinting at edges via 'how they connect', but it never sketches the return shape (nodes/edges, key names) or bounds on response size. Adequate for a read-only snapshot, but an agent cannot predict the payload structure.

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 single parameter has 0% schema description coverage, so the description carries the full burden — and it does, explaining exactly what slim=true changes (counts and types only versus full graph). This is meaning an agent could not get from the bare boolean.

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 (the Dutch Learning Graph) and enumerates its contents — providers, exams, goals and their connections — so an agent knows what comes back. It rules out a use case (recommendations), but does not distinguish itself from siblings like tdd_stats or tdd_meta that may also return aggregate data.

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 data or research tasks and not for recommendations, which gives both a when and a when-not. It stops short of naming the alternative tool an agent should reach for instead when it wants recommendations.

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