analyze_data_quality

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

Annotations already indicate read-only and open-world behavior, so the bar is lower. The description adds the three analysis dimensions but does not disclose output format, whether all dimensions are always calculated, or how 'stale' is defined beyond the parameter hint. No contradiction with annotations.

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?

A single sentence effectively front-loads the verb, target resource, and key dimensions. There is no filler or redundancy.

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?

Given the rich schema and read-only annotations, the description is largely sufficient for tool selection. However, it does not describe the return value or output structure, which would be helpful since no output schema is provided.

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 coverage is 100%, with all three parameters described, so the baseline is 3. The description adds meaningful framing by linking completeness and stale records to the parameters, but it does not provide extra syntax, defaults, or examples beyond the schema.

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 clearly states the tool analyses data quality for a table and lists the three concrete dimensions: completeness, duplicates, stale records. This distinguishes it from sibling tools such as check_table_completeness or get_table_record_count.

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 description implies use when an overall data quality assessment is needed, but it does not explicitly state when not to use this tool or mention alternatives like check_table_completeness for completeness-only checks. There is no exclusion guidance.

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