getPredictionTokenPrice

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

Annotations (readOnlyHint=true, openWorldHint=true) already establish the safety profile, and the description adds useful behavioral context beyond them: a 20-token cap per request, the response fields (bid/ask/last), and the probability-as-price semantics. It does not contradict annotations. Minor gap: no disclosure of behavior for invalid or over-limit tokenIds, but the core behavioral traits are well covered.

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?

Content is front-loaded with the core purpose, but there is clear redundancy: the 'Agent hint' paragraph repeats the probability explanation nearly verbatim ('Price is a probability (0 to 1). A YES token at 0.65 means 65% probability of YES outcome'), and 'Always check price before buy/sell' restates the earlier pre-order guidance. The description could be tightened by roughly a third without losing information.

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?

For a simple read-only quote tool with one array parameter and no output schema, the description covers purpose, return fields, parameter semantics, usage timing, and sibling routing. It lacks an exact response shape, but it names the key returned fields, which is sufficient given the low complexity. Nothing critical for a correct call is missing.

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 description coverage is 0%, so the description must compensate, and it does: it clarifies that tokenIds are specific prediction outcome tokens, ties them to the YES/NO probability interpretation, and states the maximum count of 20. This gives the agent the meaning and constraint of the only parameter. It does not explain where tokenIds originate, but the parameter is simple (array of strings) and adequately explained.

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 first sentence states a specific verb ('Query'), resource ('current market prices'), scope ('up to 20 prediction outcome tokens'), and the return content ('best bid, best ask, and last trade price'). It is clearly differentiated from the sibling getPredictionPriceHistory in the final sentence, leaving no ambiguity about what this tool does.

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 explicit when-to-use guidance ('AI agent should call this before placing orders to confirm current market prices', 'Always check price before buy/sell') and an explicit when-not-to-use with the named alternative ('Do NOT use this for price history — use getPredictionPriceHistory instead'). The agent is fully routed without inference.

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