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

Annotations already declare read-only, idempotent, open-world, non-destructive behavior, and the description adds substantial context beyond them: pricing ($0.02/call over x402 on Base), the prepaid x-credit-token header for unlimited calls, the 1 free call/day allowance, and that no API key is required. It also flags that the output is a probabilistic signal and not financial advice, which an agent needs to interpret results responsibly.

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?

Front-loaded with the core purpose and key return data, then layers on payment and access mechanics. It is somewhat dense with pricing/credit details, but those are operationally relevant to an autonomous agent and each sentence carries weight.

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 single-parameter, read-only lookup with an output schema and full annotation coverage, the description is complete: it covers what is queried, what comes back, how to authenticate/pay, and how to interpret the signal. No essential calling information 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 coverage is 100% and there is only one parameter, so the schema already carries the definition. The description adds value by providing concrete example query values ('fed', 'btc', 'kraken-ipo-in-2025'), helping the agent form a well-shaped query rather than guessing the expected granularity.

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?

States a specific verb and resource: queries Polymarket's Gamma CLOB for active prediction markets and resolves Yes/No outcome probabilities. It names the concrete data returned (probability %, 24h volume, liquidity, resolution date), which clearly distinguishes it from sibling market tools like crypto_markets or market_pulse.

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?

Gives a clear use context — 'a real-world reality check before trading, hedging, or rebalancing treasury' — which tells the agent when this tool is the right choice. However, it never names an alternative sibling tool or an explicit when-not-to-use condition, so routing is left partly to inference.

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