grok_consensus

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

The description discloses the iterative nature, default round count, and customizable rounds, but lacks details on costs (latency), model participation specifics, and limitations. With no annotations, the description carries full burden but falls short of full transparency.

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 two sentences long, front-loads the core function and return type, and avoids redundancy. Every sentence provides essential information.

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 complexity of a multi-round consensus protocol between two models, the description provides basic understanding but omits details on output structure, round mechanics, and use case scenarios. Adequate but not comprehensive.

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 description adds value beyond the schema by clarifying default rounds (3-5) and explaining the trade-off for higher round counts (deeper analysis vs. latency). Both parameters are covered in schema, so this additional context is beneficial.

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 runs an iterative Consensus Validation Protocol between two models, which distinguishes it from siblings like ask_grok (simple Q&A) and generate_image (image generation). It specifies the return type (structured final summary) and key parameters.

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 the tool is for deep analysis requiring consensus but does not explicitly state when to use it over alternatives or provide exclusions. Siblings are named but no comparative guidance.

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