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

With no annotations, the description carries the full burden; it does disclose a meaningful behavioral trait, the $0.002 USDC per-call cost, plus the fields returned. It omits rate limits, whether lookups are cached, authentication/payment mechanics, and error semantics for unresolvable queries.

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 short sentences, front-loaded with purpose, then input forms, then return contents, then cost. Every sentence adds information the agent needs and none is 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?

With no output schema, the description correctly fills the gap by naming the returned fields (probabilities, volume, liquidity, outcome prices), and it discloses cost. For a one-parameter read tool this is nearly sufficient; the missing piece is differentiation from sibling prediction-market tools.

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% and the single parameter already includes slug/ID/query examples, so the baseline is 3. The description echoes the same examples and adds no new syntax or edge-case detail (e.g., how natural language is resolved, or whether partial slugs match).

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 states a specific verb and resource ('Get current odds for a prediction market') and enumerates the lookup key types. It does not, however, distinguish itself from closely related siblings such as polymarket_quote, prediction_market_aggregator, or drift_prediction_markets, leaving the agent to infer the boundary.

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?

Usage is implied through the three accepted input forms (slug, ID, natural-language query) with examples, so the agent knows how to call it. There is no explicit when-to-use guidance versus the many alternative prediction-market tools, no exclusions, and no note on failure when a market is ambiguous or not found.

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