profile_table

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

No annotations are provided, so the description carries full burden. It implies a read operation (returning statistics) but does not explicitly confirm read-only behavior, required permissions, or whether it can be called on any table. The lack of detail about side effects or access control leaves gaps.

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?

One concise sentence (11 words) with no filler. It front-loads the verb and key outputs, making it efficient for an agent to parse.

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 description covers the core outputs (null fraction, distinct count, min/max) and assumes the output schema details the return structure. Given one parameter and no nested objects, it is mostly complete. Missing context: whether all columns are profiled or only specified ones, but that is implied.

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 0%, but the description adds 'for a table,' clarifying that the single required parameter 'table' is the table name. While minimal, it adds enough meaning beyond the raw schema. A higher score would require explicit format details, but the param is simple enough.

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 returns per-column statistics (null fraction, distinct count, min/max) for a table. It uses a specific verb ('Return') and resource ('per-column... for a table'), differentiating it from siblings like describe_table (schema) or run_sql (arbitrary queries).

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 guidance on when to use this tool versus alternatives. Siblings like describe_table and sample_rows exist, but the description does not clarify scenarios (e.g., use profile_table for data quality, describe_table for column metadata). This forces the agent to infer context.

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