nexo_learning_quality

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

No annotations are provided, so the description must fully disclose behavioral traits. It fails to specify whether the tool modifies data (it only 'scores'), what permissions are required, or what the output looks like. The phrase 'strengthened' suggests reasoning but not the action of the tool itself.

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 a single sentence, which is concise. However, it could be more structured by adding a second sentence to clarify usage or output. It is front-loaded but somewhat cryptic.

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?

Given the tool's complexity (4 optional parameters, no output schema, no annotations), the description should provide more context about return values, default behavior, and how scoring works. It only explains the 'why' but not the 'how' or 'what', leaving significant gaps.

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 100% description coverage for its 4 parameters, so the schema already documents usage. The description does not add meaningful semantic information beyond what the schema provides, thus baseline score 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 the tool's action ('Score learning quality') and its purpose ('so fragile rules can be strengthened'). It differentiates from sibling learning tools by focusing on quality scoring rather than CRUD operations. However, the term 'learning' is somewhat ambiguous without further context.

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 implies when to use the tool (before fragile rules mislead guard or retrieval), providing some context. However, it does not explicitly state when not to use it or mention alternatives like nexo_learning_search or nexo_learning_update for different needs.

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