brain_predict

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

No annotations provided, so description bears full burden. It mentions traversing the CKG and predicting failures, but does not disclose side effects, permissions, or whether the operation is read-only. The phrase 'pre-load' hints at caching behavior but remains unclear.

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?

Two sentences: first defines the tool's function, second gives usage guidance. No redundant information. Efficient and well-structured.

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 no output schema, the description states returns are 'top predicted pitfalls + highest-confidence fixes', which is adequate but ambiguous on format. With 3 clearly documented parameters and moderate complexity, the description is reasonably complete.

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% (all 3 parameters described in schema). Description adds no extra meaning beyond schema; it merely repeats or implies the purpose. Baseline 3 is appropriate.

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?

Description clearly states it is a predictive pre-fetch engine that traverses the CKG to predict failures and pre-load fixes. It distinguishes from similar siblings like 'brain_predict_failures' by mentioning 'pre-load relevant fixes' and 'highest-confidence fixes', though explicit differentiation is absent.

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 usage context: 'Call at session_start when working on a specific feature or debugging area.' However, no guidance on when not to use or alternatives like the similarly named 'brain_predict_failures'.

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