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

No annotations exist, so the description carries the full burden. It does disclose a non-obvious behavioral trait: the 0.002 USDC x402 charge per call. It omits other important traits such as statelessness, size/row limits, and what happens on malformed or headerless CSV. It also does not say whether all statistics are returned in one payload.

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, output list front-loaded, pricing appended where it belongs. No filler, though the pricing sentence is a slight tack-on rather than integrated with usage.

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?

With no output schema, the description usefully enumerates the returned metrics, which is the key thing an agent needs. What remains missing is input constraints (encoding, size, numeric-column handling) and failure behavior, which matter for a paid call.

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%, and both parameters (csv and paymentProof) are documented in the schema, including the two-step 402 flow. The description adds no semantics beyond that, so the baseline of 3 applies.

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?

Enumerates concrete outputs (per-column type detection, count, empties, uniques, min/max/mean/median/stdev for numeric columns), so the resource and operation are unambiguous. It is distinguishable from the closest sibling csv_to_json by being analysis rather than conversion, though it never names that sibling explicitly.

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?

No statement of when to use this versus csv_to_json, text_stats, or json_query, and no prerequisites such as header-row requirement or column-count limits beyond what the schema says. The only guidance-like sentence is about billing, not about tool selection.

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