Analyze NULL Patterns

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

No annotations are provided, so the description must fully disclose behavior. It mentions 'find' and 'analyze' but does not specify what the analysis entails, what the output format is, or if there are side effects (likely read-only). The description lacks details on permissions or performance impact.

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 a single, efficient sentence. It is concise but could benefit from a bit more detail without being 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?

The tool has 3 optional parameters and no output schema or annotations. The description does not explain what the tool returns (e.g., a list of columns with null percentages, patterns), nor does it provide usage context. More completeness is needed for effective tool selection.

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 each parameter is documented. The description adds no additional meaning beyond the schema; it only reiterates the tool's purpose. Baseline for full coverage is 3.

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 finds columns with high null percentages and analyzes null patterns. It distinguishes itself from sibling tools like analyze_check_constraints or analyze_data_distribution by focusing specifically on null values.

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?

No explicit guidance on when to use this tool versus alternatives. Given many similar analyze_* siblings, the description should indicate when null pattern analysis is preferred.

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