NEXO Brain

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

No annotations are provided, so the description carries full burden. It states this is an 'Add' operation (implying creation/mutation) but doesn't disclose behavioral aspects like permissions needed, whether duplicates are allowed, how the learning is stored, or what happens on success/failure. The description is minimal beyond the basic operation type.

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 perfectly structured and concise. The first sentence states the purpose, followed by a clear 'Args:' section with bullet-point parameter explanations. Every sentence earns its place with no wasted words, and the most important information (what the tool does) is front-loaded.

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 this is a creation tool with no annotations, 4 parameters, and an output schema (which handles return values), the description is mostly complete. It explains what the tool does and documents all parameters well. The main gap is lack of behavioral context (permissions, constraints, error conditions) that would be expected for a mutation tool.

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?

With 0% schema description coverage, the description provides excellent parameter semantics. It clearly explains all 4 parameters: category (with specific enum values), title (short title), content (full description), and reasoning (optional, with purpose explanation). This fully compensates for the lack of schema descriptions.

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 with specific verb ('Add') and resource ('new learning'), and distinguishes it from siblings by specifying the type of learning ('resolved error, pattern, gotcha'). It goes beyond just restating the name to explain what kind of entity is being created.

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 like nexo_learning_update or nexo_learning_search. While it's clear this creates new learnings, there's no mention of prerequisites, constraints, or relationships with other learning tools in the system.

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