nexo_learning_add

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. However, it only says 'Add a new learning' with no information on idempotency, side effects (e.g., does it trigger retroactive application?), conflict handling, auth requirements, or rate limits. This is severely lacking for a mutation tool.

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 concise sentence with no unnecessary words. However, it lacks structure (e.g., no bullet points or clear breakdown) and could be slightly more informative without becoming verbose. It earns its place but could be improved.

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 has 10 parameters, full schema documentation, and no output schema, the description is too brief. It does not mention what the tool returns (e.g., the ID of the created learning), any confirmation, or how the 'supersedes_id' or 'source_authority' parameters affect behavior. The description is insufficient for complete understanding.

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?

Schema description coverage is 100%, with all 10 parameters having detailed descriptions in the input schema. The tool description adds no additional meaning beyond what schema provides (e.g., the parenthetical 'resolved error, pattern, gotcha' is implicit from the name and schema). Baseline of 3 is appropriate as the schema does the heavy lifting.

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 purpose: 'Add a new learning (resolved error, pattern, gotcha).' It uses a specific verb ('Add') and resource ('learning'), and differentiates from sibling tools like nexo_learning_update, nexo_learning_delete, and nexo_learning_list by focusing on creation.

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 provides no guidance on when to use this tool versus alternatives. Among many sibling learning tools (e.g., nexo_learning_update, nexo_learning_search, nexo_learning_delete), there is no context for when to choose 'add' over other operations, nor any prerequisites or exclusions.

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