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

While annotations already declare readOnlyHint=true and openWorldHint=true, the description adds substantial behavioral context: it is experimental, not covered by the accuracy eval, may change or be withdrawn, costs $0.10 per call, and relies on GeckoTerminal aggregated DEX data. It also discloses that it is not an oracle or financial advice. These details go well beyond the static hints and inform the agent about reliability, cost, and data source, which are critical for decision-making.

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 a single, information-dense sentence that front-loads the critical experimental caveat before describing the tool's capabilities. It is appropriately concise for the amount of essential information conveyed (scope, data fields, data source, cost, disclaimers). While it is long, every clause serves a purpose, and the structure is logical — a minor optimization would be splitting into two sentences for readability, but it remains efficient.

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 tool's complexity (returns multiple market metrics) and the absence of an output schema, the description fully enumerates the returned data fields (price, 24h change, FDV, market cap, 24h volume, supply, pool count, deepest pool liquidity, risk flags), so an agent knows exactly what to expect. It also covers cost, data source, and experimental status. Nothing essential is missing for a correct invocation or interpretation of results.

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?

The input schema already provides 100% coverage with clear descriptions for both parameters (chain and token). The description does not add parameter-specific detail, but that is not necessary given the schema's completeness. It does imply the token is an ERC-20 contract address and chain is one of three options, but these are already in the schema. Per the baseline rule for high schema coverage, a score of 3 is appropriate.

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 clearly states the tool provides 'one-call market data for an ERC-20 token' and enumerates the specific data points (price, 24h change, FDV, market cap, volume, supply, pool count, liquidity, risk flags). This distinguishes it from sibling token_price, which presumably focuses only on price, by signaling a comprehensive due-diligence snapshot. The verb 'report' and the explicit resource scope leave no ambiguity about what the 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?

The description frames this as a comprehensive alternative to single-purpose queries ('one-call market data') and explicitly warns it is experimental, outside the core SEC path, and may change or be withdrawn. It does not explicitly name alternatives like token_price or state 'use this when you need the full picture', but the wording strongly implies it for holistic token assessments. It also mentions it is not an oracle or financial advice, which guides appropriate usage expectations. A clearer 'when to use vs. token_price' would earn a 5.

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