brain_predict

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

With no annotations provided, the description carries full burden. It explicitly states read-only nature, no side effects, no external calls, and no rate limits. It also clarifies that results are inline and not cached/persisted. This is good transparency, though it could mention error handling.

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 concise, with only a few sentences. It logically starts with the read-only nature, then explains the engine, and ends with usage and requirements. No wasted words.

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 or annotations, the description covers key aspects: read-only, no side effects, rate limits, usage timing, and parameter requirements. It could elaborate on what happens with invalid inputs or ambiguous context, but overall sufficient.

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 coverage is 100% with parameter descriptions. The description adds minimal extra meaning, only reinforcing that instance_id must be valid and providing a context example. 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?

The description clearly states it's a Predictive Pre-fetch Engine that reads the CKG to predict failures and return fixes. However, it does not differentiate from the sibling tool 'brain_predict_failures', which may cause confusion.

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 advises calling at session start when working on a specific feature or debugging area. It does not provide explicit when-not-to-use guidance or mention alternative tools for similar tasks.

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