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

Annotations already declare readOnly, idempotent, non-destructive behavior. The description adds useful context by stating that the numbers are real and come from the same key, implying data scoping and authenticity. It also guides the AI’s downstream presentation behavior, which is beyond what annotations provide.

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 description is compact and each part serves a purpose: the first sentence states the returned data, the second explains invocation timing and presentation, and the third reinforces data authenticity. No redundant wording is present.

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 parameterless read-only summary tool, the description covers what the tool returns, when to invoke it, how to interpret the data, and how to use it in conversation. Annotations handle safety, and the lack of an output schema is mitigated by the explicit field list in the description.

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 zero parameters and schema coverage is 100%, so there are no parameter semantics for the description to add. The description instead focuses on the return content and usage context, which fully compensates for the lack of parameters.

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 specifies the tool's content: school name, tariff, number of courses and published ones, students, streams, and key permissions. It also distinguishes the tool's role by stating it is called first upon connection, setting it apart from the listed siblings.

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 explicitly says the AI should call this tool first when connecting and use its data to present itself in a human-friendly way. It gives clear context for when to use the tool, though it does not explicitly mention when not to use it or list alternative conditions, so a perfect score is not warranted.

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