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

Beyond the annotations (readOnlyHint=false, idempotentHint=false), the description discloses the cost model ('One paid operation'), data handling ('no content retained'), idempotency semantics ('Reuse request_id only for identical input'), and the limits of inference ('no schema or business validity is inferred'). This explains the non-read-only annotation and conveys return semantics despite the absence of an output schema.

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?

Six short sentences, front-loaded with the purpose and output before constraints, with no filler. Each sentence adds a distinct operational fact (output shape, input exclusivity, encoding rule, cost/retention, idempotency).

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 two-parameter tool with no output schema and no annotation detail beyond flags, the description supplies the return shape, cost, retention, idempotency, and input XOR rule. An agent has everything needed to call it correctly; only the delimiter's meaning is left to the enum.

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?

With 0% schema description coverage, the description carries the burden and does well: it declares the mutual exclusion between csv_text and columns/rows, explains the string-encoding requirement for JSON numbers (to preserve leading zeros in identifiers), and governs request_id reuse. The delimiter parameter's semantics remain undocumented apart from its enum values, so it is not fully compensating.

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 states a specific verb and resource ('Profile a CSV or explicit string table') and immediately scopes it with 'before writing a recipe', which places it in the workflow relative to the preparation/recipe siblings. An agent can tell what it produces (per-column empty/distinct/duplicate counts, formula warnings) without opening the 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?

It gives clear triggering context ('before writing a recipe') and two explicit usage constraints ('Supply CSV OR columns/rows, never both', 'Reuse request_id only for identical input'). It stops short of naming a sibling such as prepare_table or validate_csv as the alternative, so the routing is implied rather than explicit.

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