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

Beyond the readOnlyHint and openWorldHint annotations, the description adds important behavioral details: the report is deterministic, no model calls are made, no data is retained, and there are explicit size limits (5 MB paid / 200 KB free). These disclosures materially help an agent understand privacy, cost, and operational constraints.

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 description is front-loaded with the core purpose and report contents, followed by concise behavioral and size-limit notes. Every sentence contributes useful information and there is no filler or redundant restatement.

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?

Although there is no output schema, the description explains the report contents (row-level examples and recommended fixes) and the operational boundaries (deterministic, no data retained, size limits). Combined with complete parameter documentation and read-only annotations, it gives an agent enough information to invoke the tool correctly.

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 input schema already documents the csv, delimiter, and hasHeader parameters. The description does not add syntax or format guidance beyond the schema, so the baseline score of 3 is appropriate.

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 resource (CSV file) and a specific output (deterministic quality report), then enumerates the exact checks included: structural errors, ragged rows, inferred types, blanks, invalid emails/URLs/phones, duplicates, and candidate keys. This distinguishes it from siblings such as validate_json and verify_citations, which target different resources.

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?

The context implies this tool is for assessing CSV data quality, but there is no explicit when-to-use/when-not-to-use guidance and no routing among sibling tools. It supplies size limits, which helps set expectations, but an agent must infer that this is the right tool for CSV quality versus, for example, JSON validation.

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