profile_table

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the description need not repeat those. It adds useful behavioral detail: numeric-only mean/stddev, low-cardinality top-5 values, and optional column restriction. No contradiction with annotations exists.

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, compact sentence with no filler. It front-loads the core output, then adds the optional restriction. Every word contributes information.

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 low parameter count, straightforward optional behavior, safe annotations, and presence of an output schema, the description provides enough for an agent to invoke the tool and understand the result shape. It lacks explicit sibling differentiation and table-name semantics, but these are minor for this simple read-only profiler.

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 must compensate. It partially clarifies the columns parameter as an optional restriction, but it never explains that name refers to the table being profiled nor any expected naming/format details. This leaves a meaningful gap for one of only two parameters.

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 identifies the tool as producing per-column statistics and enumerates specific outputs: count, null %, distinct, min, max, mean/stddev, and top-5 values. This distinguishes it from describe_table and sample_rows despite not using an explicit verb. It would be a 5 with a direct action verb like 'Compute'.

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 use case is implied: use this when you need column-level statistical summaries and optionally want to restrict to certain columns. However, there is no explicit guidance on when to prefer this over describe_table, sample_rows, or query, and no alternatives are named.

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