consortium_chat

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

With no annotations, the description must carry the full transparency burden, but it only reveals that the tool collects responses from multiple models. It does not disclose potential side effects, network costs, latency, or whether it writes to a dataset. Given the tool's complexity and 17 parameters, this is insufficient behavioral disclosure.

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, economical sentence without redundancy. It front-loads the core action. However, it is perhaps too terse for a tool of this complexity, so it loses one point for not using the structure to layer essential 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?

Despite having an output schema, the description leaves the tool's context highly under-specified: no explanation of what 'ground truth' means, no integration hints with sibling tools like autotune_analyze or parseltongue_encode, and no prerequisites. For a tool with 17 parameters and no annotation support, this is far from 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 schema has 0% description coverage, and the description provides no parameter meaning either. It does not explain the purpose of non-obvious parameters like godmode, parseltongue, or strategy, nor does it clarify the expected format of messages. The description completely fails to compensate for the schema's lack of annotations.

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 a verb+resource action: 'Collect responses from all tier models and synthesize ground truth.' This distinguishes it from sibling tools like single_chat by emphasizing multi-model aggregation. However, 'ground truth' is somewhat vague, preventing a perfect score.

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?

No explicit guidance is provided on when to use this tool versus alternatives like single_chat or ultraplinian_chat. The description implies a consensus-oriented use case but lacks any context for prerequisites, exclusions, or criteria. This is a significant gap for a tool with many specialized siblings.

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