gpu_spot_prices

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It correctly indicates a read-only operation (returns data), mentions live prices, interruption probabilities, on-demand comparisons, and optional forecasts. This is sufficient for an agent to understand the tool's behavior, though it could be slightly more explicit about the real-time nature.

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?

Three sentences: first states purpose and scope, second lists key features, third provides usage guidance. No redundant or extraneous information. Every sentence is informative and earns its place.

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?

The description covers the main points: providers, interruption probabilities, comparisons, and optional predictions. No output schema exists, but the description hints at the return format (prices, etc.). A slightly more detailed account of what exactly is returned (e.g., sorted list, structured data) would improve completeness, but it is still adequate.

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?

Input schema coverage is 100%, with each parameter described. The description adds context beyond the schema (e.g., 'optional 1hr/4hr price forecasts with buy/wait recommendations') but largely reiterates the schema. The baseline of 3 is appropriate given high schema coverage.

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 it returns live GPU spot prices across multiple providers, with specific additional features (interruption probabilities, on-demand comparisons, forecasts). It ends with a clear usage directive: 'Use this to find the cheapest available GPU slot.' This distinguishes it from sibling tools like analytics or compound_lookup, which are unrelated domains.

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 when to use the tool ('find the cheapest available GPU slot') but does not explicitly mention when not to use it or provide alternatives. However, the sibling tools are in very different domains (analytics, literature, patents), so the context is clear enough for an AI agent to select this tool appropriately.

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