Global prediction-market volume trend

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

Beyond the annotations (readOnly, openWorld), the description discloses key behaviors: data is captured forward from 2026-07-02, bounded to a rolling ~90-day window, gaps are nulls rather than zeros, and play-money venues are excluded. This is rich context that prevents misinterpretation of the data.

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 dense but efficient, with each sentence adding critical information: computation method, venue exclusions, gap semantics, window bounds, and access requirements. No filler or repetition.

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 output schema is present, the description doesn't need to detail return fields. It fully covers the tool's temporal coverage, data granularity, venue scope, and null behavior, making it sufficient for an agent to select and invoke correctly.

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 tool accepts no parameters (empty input schema), so the description need not elaborate on parameters. The baseline for zero-parameter tools is 4, and the description appropriately focuses on output semantics rather than inputs.

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 provides a global daily prediction-market volume trend, specifying the computation method (day-over-day delta of cumulative volume), venue inclusion (real-money only), and intended use (assess growth/shrinkage). This distinguishes it from sibling data tools that likely provide different metrics.

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 explicitly says 'Use this to see whether cross-venue prediction-market activity is growing or shrinking over time,' giving a clear intended use. It also notes 'No API key required,' which is access guidance. However, it does not mention alternatives or when-not-to-use scenarios, so it stops short of a 5.

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