get_distribution

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

Beyond the readOnlyHint annotation, the description adds meaningful behavioral detail: it returns bin counts only, defaults to 20 bins, uses type-specific binning strategies, and never returns the underlying rows. This gives the agent a clear picture of output scope and side-effect profile. It could add a bit more about empty/missing-value handling, but the stated behavior is already strong.

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 compact and front-loaded with the core purpose. The categorical mapping is dense but understandable. The mysterious 'B8' tag is the only element that does not add immediate value for an agent, which keeps this from a perfect score.

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?

With no output schema, the description does enough by stating that it returns bin counts and not rows, and by describing the binning rules for each type. It doesn't fully specify the exact response shape or edge-case handling, but for the intended use it is sufficiently complete.

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?

The input schema covers 100% of parameters, but the description adds important semantic nuance beyond the schema, such as the default bin count of 20 and how the bins parameter applies differently depending on column type. This enriches parameter understanding without redundant repetition.

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 states a specific purpose ('Unified bin-counts for any column type') and precisely distinguishes the output from row-returning tools by saying it 'never returns the underlying rows'. The type-by-type behavior (numeric equal-width bins, datetime time-bucket bins, categorical top-n + 'other') makes the tool's function unmistakable and clearly differentiates it from siblings like get_rows or sample_rows.

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 gives clear usage context: it is a 'Token-cheap way to ask what does this column look like?'. This implies when to use it, but it does not explicitly name alternatives or state when not to use it. It provides enough context for an agent to select it for column distribution exploration, though it could be stronger with explicit exclusions.

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