Brain Learn

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

Annotations indicate readOnlyHint=false and openWorldHint=false, so the tool mutates state. The description adds behavioral context: it stores 'methods and outcomes only' and explains the downstream effect (ranking and suppression). It doesn't disclose side effects like whether existing data is overwritten or if there are idempotency constraints, but the key behavioral traits are covered.

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?

Four sentences with no redundancy. The first sentence establishes the core action, the second explains the purpose, the third gives usage context, and the fourth clarifies storage scope. Information is front-loaded and each sentence 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?

The tool has an output schema, so return values are covered. The description explains when to use, what it does, and what it stores. It lacks explicit parameter documentation, but given the tool's simplicity and the presence of an output schema, it's largely complete. The main gap is parameter semantics, but that's already accounted for in that dimension.

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 0%, so the description must explain parameters. It defines outcome values (HIT, CLEAN, FALSE_POSITIVE) and mentions 'methods' (likely technique) and outcomes, but it doesn't explicitly describe vuln_class, waf_vendors, or technologies. While it gives some meaning to outcome, the other parameters remain ambiguous. The description partially compensates but not fully.

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: teaching the brain an observed outcome (HIT, CLEAN, FALSE_POSITIVE) to influence future plan ranking and suppress false-positive tools. It distinguishes itself from sibling tools like brain_recall (querying) and brain_state (viewing) by emphasizing the 'learn' action. Specific verb 'Teach' and resource 'neuron brain' with concrete outcomes.

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?

Provides explicit triggers for use: 'Use after a validator run, or when triage drops a finding.' This clearly indicates when to call the tool. It doesn't mention alternatives or when not to use, but the context is specific enough for an agent to decide. It explains the benefit of teaching false positives, which reinforces the use case.

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