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

With no annotations, the description must carry the full behavioral burden; it does disclose useful traits: determinism, that every issue cites its exact row number, and a per-call price ('$0.01/call, USDC, eip155:8453'). That pricing and determinism detail is genuinely valuable, but the return payload shape (issue list structure, severity, pass/fail signal) is never described and there is no output schema to compensate.

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 check list is dense and front-loaded, and the pricing/cost note is useful. The third sentence ('Use this when an agent needs to structural lint: ragged rows, dupes, quoting issues') largely re-lists issues already enumerated, making it mild redundancy rather than earning its place.

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 validator with no output schema, the description should convey what a caller gets back; it only says issues cite row numbers, leaving the result structure and pass/fail signaling unstated. It also omits how expectedColumns is encoded, which is a real gap given all three params are required.

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%, so the schema already documents all three parameters and the baseline is 3. The description adds only marginal meaning, hinting that column counts are checked 'against the header or an expected schema,' which loosely maps to expectedColumns, and implying RFC 4180 delimiter handling; it does not explain the expectedColumns string format.

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+resource ('Validate CSV structure') and enumerates the exact checks performed: RFC 4180 parsing, ragged rows, column-count consistency, duplicate headers, stray quotes, empty-cell counts. This clearly distinguishes it from sibling validators like dev_json_schema_validator and dev_csv_convert.

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 explicit context for invocation ('Use this when an agent needs to structural lint') and the pre-ingestion use case is front-loaded. However, it never names the alternative (e.g., dev_csv_convert) or states when NOT to use it, so routing between the two CSV tools is left to inference.

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