Get Learning Narratives

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

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds useful behavioral context beyond that: results are AI-generated, periodic, anonymized, and include stats. It does not describe the exact return shape, but the description still adds meaningful value beyond the annotation.

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 concise sentences with no filler. The main action and resource are front-loaded, and the second sentence adds a legitimate use case. Every part 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?

For a simple read-only tool with one optional parameter and an annotation covering safety, the description is nearly complete. It explains what the tool returns and why an agent would use it. It could be slightly stronger with an explicit mention of sibling alternatives, but no critical information 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 input schema has full coverage for the single 'limit' parameter, including default, min, and max. The description does not add much parameter-specific detail beyond the schema, so the baseline of 3 is appropriate.

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 states a specific verb ('Returns') and resource ('AI-generated learning narrative summaries') with meaningful detail about what these are: periodic reflections on learner progress with stats. It implicitly distinguishes this tool from siblings like session/proficiency summaries, though it does not name an alternative explicitly.

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 gives a concrete use case: 'useful for auditing narrative quality and checking if the reflection system captures meaningful patterns.' This provides clear context for when to call the tool, though it does not explicitly discuss when not to use it or name sibling alternatives.

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