pplx_council

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

With no annotations, the description fully discloses behavior: parallel queries to multiple models, synthesis by chairman, cost per model, default settings, and parameter effects (e.g., synthesize, thinking, chairman). It covers all behavioral traits.

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 well-structured with a clear heading, important notes, and parameter list. It is front-loaded with the purpose. Slightly verbose but every sentence adds value; could be tightened slightly.

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 complexity, the description is comprehensive: it explains the workflow, cost, available models, defaults, and parameter options. The presence of an output schema reduces the need to describe output, so completeness is excellent.

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?

Despite 0% schema coverage, the description provides detailed semantics for all six parameters: query, models (with list of available values and default), source_focus, synthesize, thinking, chairman (with default and cost note). It adds meaning beyond the schema.

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 purpose: 'query multiple models in parallel, get synthesized consensus.' It distinguishes itself from sibling tools (individual model tools like pplx_gpt54, pplx_claude_opus) by being a meta-tool that aggregates responses.

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 provides explicit step-by-step usage instructions, including telling the user about available models, asking for model selection and count, informing cost implications, and requiring explicit confirmation. It also gives a default council composition.

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