predict_ensemble

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

Description adds context beyond annotations (e.g., returns decomposed uncertainty, agreement score, weight share, entropy). Annotations already cover readOnly, destructive, idempotent, openWorld. No contradictions.

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 and output. The word 'Free.' at end is slightly ambiguous but not misleading. Efficient overall.

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?

Covers purpose, usage, output, and differentiation. Has output schema available. Could mention minimum predictions requirement 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%, so baseline 3. Description does not add significant meaning beyond schema; it mentions 'heterogeneous predictors' but doesn't elaborate on parameter details.

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 'Combine N model predictions into a single consensus value' and distinguishes from sibling 'score_convergence' by specifying use case for heterogeneous predictors.

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 says when to use (fuse outputs from heterogeneous predictors) and provides an alternative: 'For fusing source-agreement on a probability of one event, use score_convergence.'

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