Token price

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

Annotations already provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations: the near-live cached nature ('cached for about a minute, so figures are near-live but not a tick-by-tick trading feed') and the empty-data behavior for tokens with no liquidity pool ('returns empty market data rather than an error'). This is genuinely useful for an agent deciding whether to call this 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?

The description is compact, information-dense, and logically ordered: what it returns, freshness, edge behavior, usage context, exclusions, and cost. Every sentence earns its place, and the most decision-relevant facts (what data, non-live nature) are front-loaded. No filler or repetition of schema fields.

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 annotations cover read-only/idempotent safety, the schema covers parameters, and the output schema presumably defines the return shape, the description covers all remaining operational context: freshness, empty-result behavior, use cases, exclusions, and cost. An agent has everything needed to invoke it correctly and avoid the risk-specific siblings.

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 100%, so the schema already documents both parameters thoroughly. The description adds value by giving context about the market-data aggregation source and by connecting the parameters to usage context ('Arguments: address and chain; one API unit on any chain'), but most parameter semantics are already in the schema. A 4 reflects that the description doesn't materially extend param meaning but does reinforce the cost implication.

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 opens with a precise verb-resource pairing ('Current market data for one token') and enumerates the exact fields returned (price, market cap, liquidity, volume, recent OHLCV candles). It clearly distinguishes this tool from siblings like get_liquidity or get_rug_score by stating what it is not: not a risk signal and not a long price history feed.

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?

Explicit when-to-use guidance is given: 'Use it for the market context around a risk verdict.' It also provides explicit when-not-to-use guidance: 'Not for risk of any kind — market data carries no forensic signal — and not for long price history beyond the recent candles.' With sibling tools like get_rug_score and get_token_scan, this exclusion is essential and it is clearly stated.

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