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

No annotations are present, so the description carries the behavioral disclosure burden. It adds valuable context such as pay-per-call pricing and the categories of data returned. However, it does not disclose potential failure modes, data freshness guarantees, auth requirements, or whether the call mutates anything or is read-only, leaving some agent-facing ambiguity.

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?

Three sentences, each earning its place: what it returns, when to use it, and cost. The main deliverable is front-loaded in the first sentence. Minor structure improvements could separate pricing into a more prominent callout, but overall it is tight and readable.

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 one parameter, no output schema, and no annotations, the description provides a solid picture: purpose, included data categories, use case, and cost. It does not describe the output shape or error behavior, but the listed report contents approximate the return set well enough for a single-mint research tool.

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 schema covers the single `mint` parameter with a clear description ('SPL token mint address'), so schema coverage is 100%. The description does not add meaning beyond naming the token context, but none is strictly needed for a single, well-documented parameter.

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 a specific verb/resource ('Full token due-diligence') and enumerates concrete items (live price, liquidity, 24h volume, DEX pairs, supply, mint authority). It also distinguishes itself from 'multiple lookups,' making clear this is an aggregator tool rather than a single-purpose lookup, which helps separate it from siblings like solana_token.

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?

States a clear use case: 'Use for token research instead of multiple lookups.' This implies it is the preferred choice when broad token data is needed and that narrower tools may be more appropriate for single metrics, though it does not explicitly name alternatives or when not to use it.

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