ensemble_vote

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

No annotations exist, so the description carries the full burden of behavioral disclosure. It does reveal one notable trait — automatic model-tier selection (hinting the tool may override the models parameter) — and states the output shape (consensus + confidence). But it omits failure behavior when consensus cannot be reached, cost/latency implications of calling multiple models, and any caveats.

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 short sentences, each contributing a distinct fact (the action, the auto-selection behavior, the return shape). No filler or redundant restatement of the tool name. Content-to-length ratio is good even if the structure risks skimming over needed parameter details.

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?

This is a 4-parameter tool with a required enum, a free-form nested context object, no output schema, and an auto-selection behavior that interacts ambiguously with the models parameter. The description does not explain the task_type enum values, the purpose of context, or how models relates to the auto tier selection. For this complexity, the description is too thin.

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 0%, so all four parameters (models, prompt, context, task_type) are undocumented in the schema and the description must compensate. It explains none of them — nothing about what models accepts, what context supplies, or what values task_type expects. The description adds zero meaning over bare parameter names.

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 states a specific verb-resource pair ('Run ensemble voting across multiple AI models') and lists two concrete behaviors: selecting an optimal model tier and returning a consensus decision with a confidence score. No sibling tool offers ensemble voting, so it is readily distinguishable by subject matter, though it never explicitly contrasts any sibling.

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?

There is no when-to-use guidance, no named alternatives, and no exclusions. The phrase 'Selects optimal model tier based on task complexity' implies automatic behavior but never tells an agent when to choose this tool over siblings like ask_marketic or the generate_* tools.

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