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

Annotations already cover readOnlyHint=true and openWorldHint=true, so safety is handled. The description adds two meaningful traits the annotations don't: it is free, and the data is 24h stale. Staleness is exactly the kind of behavior an agent needs to avoid treating it as live data.

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?

One sentence, front-loaded with 'Free.' and then the content and rationale. Every clause earns its place with zero filler.

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 parameterless, read-only snapshot tool with no output schema, the description supplies enough to call it correctly: what the data is, how old it is, and why you'd use it. It doesn't describe the shape of the returned market data, which is the only modest gap.

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 tool takes zero parameters, so there is no parameter semantics to document and the schema is trivially complete. Baseline of 4 applies; the description correctly implies the call is parameterless.

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 resource (the crowd_market view) plus a concrete modifier (as it was 24 hours ago), and names the sibling crowd_market it samples from. An agent can distinguish it from crowd_market and the other market tools without opening a schema.

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?

Explicitly frames the use case: 'to evaluate the data before paying,' which implies crowd_market is the paid alternative to graduate to. Clear context for when to call this, though it doesn't spell out exclusions (e.g., not for production/live decisions).

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