brain_predict_failures

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

No annotations provided, so description carries full burden. Discloses it returns 'top likely failure modes ranked by probability, with pre-loaded fixes' and uses 'CKG causal edges + lesson history'. No destructive behavior implied; reasonable transparency for a predictive tool. Missing details on authorization or rate limits.

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 efficient sentences: first states core purpose, second gives usage examples, third adds data source and broad use case. No fluff, front-loaded with key information.

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?

No output schema, but description summarizes return as 'top likely failure modes ranked by probability, with pre-loaded fixes'. Covers purpose, usage, data sources, and output. Slightly vague on exact output structure, but adequate for a predictive tool with 4 parameters.

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 has 100% description coverage, so baseline is 3. Description adds value by explaining the 'context' parameter with examples and noting that predictions come with 'pre-loaded fixes', which is not in the schema. This additional context justifies a 4.

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?

Clearly states 'Pre-deploy failure prediction with probability percentages' and provides concrete examples like 'upgrading Keycloak 21→24'. Distinguishes from siblings such as brain_predict and brain_diff by specifying domain (pre-deploy failures) and data sources (CKG causal edges + lesson history).

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 advises 'Call before any significant deploy, migration, or infrastructure change' and gives example contexts. Does not explicitly mention when not to use or provide alternatives, but the context is clear enough for an agent.

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