check_data_quality

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

With no annotations, the description must carry the behavioral disclosure burden. It does state the return type ('JSON string with data quality report') and the kinds of issues detected, but it does not disclose read-only behavior, thresholds, handling of missing variables, or what counts as a 'suspicious pattern'.

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 compact and front-loaded, with clear Args and Returns sections and no filler. It earns a strong score for structure; it loses one point only because the parameter explanations are thin and could add useful detail without becoming verbose.

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 analysis tool with an output schema, the description covers the essential invocation details: what file to point at, what variable to check, and what kind of result is returned. It is not fully complete because the semantics of 'suspicious patterns' and outlier detection are left undefined, forcing the agent to discover behavior at call time.

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 0%, so the description's Args section is the only parameter documentation. It adds minimal but real meaning: file_path is a path to a NetCDF file, and variable_name is the variable to check. It does not specify how to locate the variable within the file or validate it, but the basic semantics are sufficient for correct parameter identification.

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 opens with a specific verb and resource ('Check a variable for data quality issues') and enumerates concrete checks: NaN/missing values, outliers, and suspicious patterns. This makes it distinguishable from statistical or structural sibling tools, though it never explicitly names an alternative.

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 phrasing implies this tool should be used when an agent needs a quality assessment rather than statistics, metadata, or variable explanations. However, there is no explicit when-to-use vs. when-not-to-use guidance and no mention of how it compares to close siblings like get_variable_stats or check_cf_compliance.

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