Multi-Model Consensus

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

Annotations provide only openWorldHint: true, which is vague. The description adds behavioral detail (parallel querying, synthesizing) but does not disclose potential costs, latency, rate limits, or failure handling for individual model calls. It goes beyond annotations but still lacks deeper transparency for a multi-call tool.

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, tightly written sentence that conveys the core functionality without any redundancy or filler. Every word contributes to understanding the tool's purpose and mechanism.

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 the tool's moderate complexity (6 parameters, no output schema), the description adequately explains what it does and how, and the schema covers parameter details. However, it lacks explicit guidance on when to use this tool over sibling options and does not describe possible return format or error scenarios, so it is not fully 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?

The input schema has 100% description coverage for all six parameters, so the schema fully documents parameter semantics. The description offers no additional parameter-level insight, warranting the baseline score of 3 per the rubric.

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 the tool's function: "Generate a consensus completion by querying multiple distinct models in parallel and synthesizing their responses using a synthesizer model." It specifies a concrete verb (generate), a resource (consensus completion), and the method (parallel multi-model querying), which distinguishes it from siblings like chat_completion or chat_routed.

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 implies its use case (multi-model consensus) but does not explicitly state when to use it versus alternatives such as chat_completion or chat_routed. No exclusions or alternative recommendations are provided, leaving the agent to infer the appropriate context from the tool's name and description.

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