analyze_column_distribution

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

With no annotations, the description discloses key behaviors: max 10,000 records, default sample size 1000, works with numeric/string/date columns, automatic type detection, and specific statistics computed (percentiles, IQR). It omits permission requirements or side effects, but for a read-only analysis tool, this is sufficient.

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 well-structured with sections and front-loaded with purpose, but it is verbose and contains some repetition (e.g., 'data quality' appears multiple times). Could be more concise without losing clarity.

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 5 parameters, no output schema, and no annotations, the description covers purpose, parameter details, use cases, performance notes, and expected output statistics. It fully enables an agent to select and invoke the tool correctly without needing external context.

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 baseline is 3. The description adds marginal value by explaining sample_size trade-offs and default values, but these are already in the schema. Additional context like use cases does not directly enhance parameter understanding.

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 performs advanced statistical analysis of a column's data distribution, including nulls, distinct values, percentiles, and outlier detection. It distinguishes itself from sibling tools like get_table_schema or find_assets_by_column by focusing on distribution analysis rather than metadata or search.

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 provides explicit when-to-use scenarios (e.g., data quality checks, profiling, outlier detection) and example queries. However, it does not explicitly state when not to use this tool or contrast with alternatives like querying directly, but the context is clear.

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