brain_predict

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

With no annotations, the description fully discloses that the tool is read-only, has no side effects, no external network calls, no rate limits, and results are inline (nothing cached or persisted). It also explains scope fallback behavior. Missing is the output format or structure, but overall transparency is high.

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 a single, well-structured paragraph. It front-loads the safety disclaimer, then explains purpose, usage context, and scope details in a logical flow. Every sentence adds value, and there is no redundant or extraneous content.

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 description covers safety, usage timing, and parameter behavior well, but the absence of an output schema means the agent is left without any description of the return format (e.g., structure of predictions, confidence levels). This gap is significant for a prediction tool, making completeness only adequate.

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%, so baseline is 3. The description adds valuable functional meaning to the scope parameter (e.g., 'instance = this brain only (today’s behaviour)', 'org = cross-team Org Knowledge Graph'), which goes beyond the enum values in the schema. Top_k and context are not enhanced, but the scope explanation warrants 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?

The description clearly defines the tool as a read-only Predictive Pre-fetch Engine that reads the CKG to predict failures and return highest-confidence fixes. It uses specific verbs and resources, and distinguishes itself from siblings by emphasizing its read-only nature and inline results without persistence.

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?

Explicit guidance is given: 'Call at session start when working on a specific feature or debugging area.' It also advises when to widen scope to org. However, it does not directly compare to alternatives like brain_predict_failures or brain_plan, leaving room for confusion among similar sibling tools.

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