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

No annotations are provided, so the description carries the full behavioral burden. It discloses the paid nature of the tool—0.001 USDC per call via x402—and the schema explains the 402 payment flow, which covers the most important operational behavior for this simple read-only data tool.

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?

Two sentences front-load the specific data returned and follow with the payment cost. There is no redundant or filler text, and the structure is immediately useful to an agent deciding whether to call it.

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 no-annotation, no-output-schema read tool, the description states the metrics, source, and cost, while the schema covers payment mechanics. It is complete enough to invoke correctly, though it could briefly clarify how it differs from sibling crypto data 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?

There is only one parameter, paymentProof, and the schema description coverage is 100%, so the schema already explains when to omit it and how to supply proof after payment. The description adds no parameter-level meaning beyond the schema, which is acceptable at baseline 3.

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 names the specific global crypto metrics returned—market cap, 24h volume, market-cap change, and BTC/ETH dominance—plus the data source CoinGecko. It is clearly distinct from price-focused siblings such as crypto_price, but it does not explicitly contrast itself with those siblings.

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 by 'Global crypto market cap' versus token-level tools like crypto_price or crypto_categories. However, there is no explicit when-to-use statement, no exclusion of alternatives, and no guidance on whether this is for aggregate market snapshots versus specific asset data.

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