consensus

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions synthesizing consensus but doesn't explain how the synthesis works (e.g., voting, averaging, reasoning), what the output format is, whether there are rate limits, or if authentication is required. For a tool that queries multiple external AI services, this lack of operational details is a significant gap.

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 extremely concise and front-loaded: two sentences that directly state the tool's purpose and usage guideline. Every word earns its place, with no redundant or vague phrasing. It efficiently communicates the core functionality without unnecessary elaboration.

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 querying multiple AI models and synthesizing results, the description is incomplete. There's no output schema, and the description doesn't explain return values or behavioral traits. With no annotations and a non-trivial operation, the description should provide more context about how consensus is achieved, error handling, or response structure to be 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?

The schema description coverage is 100%, with both parameters ('query' and 'detailed') well-documented in the schema. The description adds no additional parameter semantics beyond what's in the schema. According to the rules, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description.

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: 'Get multiple AI perspectives (Claude, GPT, Gemini) and synthesize consensus.' It specifies the verb ('get', 'synthesize'), resource ('AI perspectives'), and scope ('multiple' across three named models). However, it doesn't explicitly differentiate from sibling tools like 'think' or 'openrouter_chat', which might offer similar AI analysis capabilities.

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 clear context for when to use it: 'Use for important decisions.' This gives practical guidance on appropriate scenarios. However, it doesn't specify when NOT to use it or mention alternatives among sibling tools (e.g., 'think' for single-model reasoning or 'openrouter_chat' for other AI models), leaving some ambiguity in tool selection.

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