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), so the description's job is to explain the payload; it does so by naming the three content categories returned. It does not describe output size or format, which matters somewhat since there is no output schema.

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 the imperative call-to-action front-loaded and the rationale following. No redundant restatement of the title or name.

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 zero-parameter, zero-side-effect tool with no output schema, the description is sufficient to call it correctly and to understand what comes back at a high level. A note on the prompt's approximate size or whether it should be re-read each session would close the remaining gap.

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 is nothing for the description to disambiguate; the schema coverage of 100% on an empty object confirms nothing is hidden. Baseline for a no-param tool applies.

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 names a concrete resource — the AILANG teaching prompt — and enumerates its contents (syntax rules, templates, stdlib map), so the agent knows exactly what it gets. It does not explicitly contrast with the similarly named sibling prompt_get, so sibling differentiation is left to inference rather than stated.

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?

"Call this FIRST" is an explicit ordering instruction, and the second sentence gives the condition that motivates it: AILANG is not well represented in training data. There is no when-not guidance or named alternative, but for a mandatory-prerequisite tool the usage context is clear.

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