brain_predict_failures

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

With no annotations, the description fully carries behavioral disclosure. It explains it uses 'CKG causal edges + lesson history' and returns ranked failure modes with fixes. Lacks details on error conditions or response format, but adequate for a prediction 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?

Three sentences, front-loaded with purpose, no redundant information. Every 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?

Tool has 4 params (100% schema coverage), no output schema. Description adequately explains return value as 'top likely failure modes ranked by probability, with pre-loaded fixes.' Could be more detailed on output format, but sufficient for a prediction 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?

Schema description coverage is 100%, so baseline is 3. The description does not add additional meaning beyond what schema provides for parameters; it only repeats the 'context' example from schema.

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 'Pre-deploy failure prediction with probability percentages' and specifies inputs (change context) and outputs (failure modes with probabilities and fixes). It differentiates from sibling 'brain_predict' by focusing on failures and deployment readiness.

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?

Explicitly states 'Call before any significant deploy, migration, or infrastructure change.' Provides clear context but does not mention when not to use or suggest alternatives among siblings.

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