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

With no annotations, the description carries the full burden. It discloses key behavior: returns the best-matching market with implied probabilities and source links, which is useful. However, it does not mention edge cases such as no matching market, ambiguous questions, or rate limits.

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 sentences, each earning its place: function, usage trigger, example, return behavior. Information is front-loaded and there is no redundant filler.

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 tool's simplicity (one parameter, no nested objects) and the presence of an output schema, the description is nearly complete. It defines purpose, when to use, example input, and output contents. A small gap is lack of failure-mode behavior, but not enough to lower to a 3.

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 only names the parameter 'question' with no description coverage. The tool description compensates by explaining the expected format: a natural-language question about a future event, and provides a concrete example. This adds real semantic value beyond the schema.

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 begins with a specific verb+resource ('Get live prediction-market odds') and clarifies scope ('real-world event, phrased as a natural language question'). The examples and 'best-matching market' language help distinguish this general tool from event-specific siblings like get_world_cup_odds.

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?

It explicitly states when to call ('when the user asks about the probability, odds, or likelihood of any future event') and gives concrete categories. It does not mention exclusions or alternative tools, but the trigger conditions are clear and well-scoped.

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