sharpsignal_predict

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

With no annotations provided, the description carries full burden and discloses critical behavioral traits: monetary cost per invocation, specific three-part output structure (bull case, bear case, implied probability), reliance on live web search, and underlying model (sonar-reasoning-pro). Missing rate limits or error handling details.

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?

Four efficient sentences with zero waste: market positioning ('Prediction market intelligence'), core mechanics, attribution, and cost warning. Critical cost information is front-loaded in the final sentence where it won't be missed.

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?

Compensates well for the missing output schema by explicitly describing the structured return format (bull/bear/probability) and including cost data. Adequately covers the single parameter's intent. Would be a 5 if it mentioned behavior for ambiguous/non-yes/no inputs.

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 coverage is 100% with the 'prompt' parameter fully documented in the schema including an example. The description reinforces the yes/no constraint but does not add syntactic or semantic details beyond what the schema already provides, warranting the baseline score.

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?

Description uses specific verb+resource ('Submit any yes/no question and get back...') and clearly distinguishes from bittensor siblings by specifying 'Powered by Perplexity sonar-reasoning-pro', indicating this is a third-party prediction service rather than on-chain Bittensor inference.

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?

Provides clear context for when to use (yes/no prediction market questions requiring bull/bear case analysis) and includes cost information ($0.25) that guides selection. Lacks explicit comparison to sibling 'bittensor_forecast', but the Perplexity attribution and cost data provide implicit differentiation.

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