NEXO Brain

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. It mentions the tool checks learnings but doesn't specify what 'learnings' entail (e.g., past errors, best practices), how results are returned, or any side effects like rate limits or permissions needed. This leaves significant gaps in understanding the tool's behavior beyond its basic purpose.

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 extremely concise and front-loaded with essential information in just two sentences. Every word serves a purpose: the first sentence states what the tool does, and the second provides critical usage timing. There is no wasted text, making it highly efficient.

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 an output schema (which should cover return values), the description's main gaps are in parameter semantics and behavioral details. It adequately conveys the purpose and usage timing, but without annotations and with poor schema coverage, it doesn't fully equip an AI agent to use the tool effectively in all contexts.

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 3 parameters with 0% description coverage, and the tool description provides no information about what 'files', 'area', or 'include_schemas' mean or how to use them. This forces the AI agent to guess based on parameter names alone, which is insufficient for reliable tool invocation.

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: to check learnings relevant to files/area before editing code. It uses a specific verb ('check') and resource ('learnings relevant to files/area'), making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'nexo_learning_search' or 'nexo_learning_list', which might also involve learnings, so it doesn't reach a perfect score.

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 explicit usage guidance: 'Call this before any code change.' This clearly states when to use the tool (before editing code) and implies it should not be used for other purposes. It doesn't name alternatives, but the context is sufficiently clear for an AI agent to apply it correctly.

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