fusion

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

With no annotations provided, the description carries full burden. It fully discloses the parallel execution, judge synthesis, and output structure. It also lists available models and default panel/judge, giving the agent a clear behavioral model of the tool's operation.

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 moderately long but well-structured: first sentence defines core functionality, followed by usage guidance and a clear list of available models. Every sentence adds value, though the model list could be slightly more compact.

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 3 parameters (all documented), no siblings, and no output schema, the description covers all needed context: purpose, process, model options, defaults, and use cases. It is fully sufficient for an agent to select and invoke the tool correctly.

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 value by specifying default panel_models and judge_model, which are not in the schema. This helps the agent understand optional parameters' typical usage beyond the schema's field descriptions.

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 begins with 'Multi-model fusion analysis' and clearly states the process: parallel query to multiple models, judge synthesis into structured analysis. It specifies output categories (consensus, contradictions, etc.), making the tool's unique value distinct from single-model tools. No siblings are provided, so distinction isn't needed.

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 recommends this tool 'for complex research questions, multi-perspective analysis, or when accuracy is critical.' While it doesn't mention when not to use it or list alternatives, the context of no siblings makes this guidance sufficient for an agent to decide when to invoke.

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