Ask The Panel

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

With no annotations provided, the description carries the full behavioral disclosure burden. It explicitly mentions parallel execution and per-slot error fields for failed or unconfigured providers, which is important operational context. It does not mention potential latency, cost, or rate limits, but for a straightforward aggregation tool these are not critical gaps.

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?

Two sentences with zero fluff. The action and the return format are stated immediately, and the error-handling note is placed after the primary behavior. Every sentence earns its place.

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?

For a tool with 3 parameters and an output schema, the description covers the essential behavior and error handling. It does not explain prerequisites like API configuration, but that is implied by 'enabled providers'. The return format is handled by the output schema, so the description is adequately 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?

Schema description coverage is 100%, so all three parameters are already well-documented in the schema. The description adds no new semantic value beyond what the schema provides, such as example formats or edge-case guidance. This meets the baseline for high coverage.

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 uses a specific verb ('Convene') and resource ('a panel of all enabled rival LLMs'), and clearly differentiates from the sibling 'ask_other_model' by emphasizing the parallel, multi-provider nature. It also describes the return value as labeled answers with per-slot error fields, leaving no ambiguity about what the tool does.

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 usage for gathering multiple perspectives from all enabled providers in parallel, which contrasts with the single-model sibling 'ask_other_model'. However, it does not explicitly state when to choose this over the alternative or provide exclusions, leaving some room for inference.

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