get_data_hotspots

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

Beyond the readOnlyHint annotation, the description discloses the fallback behavior when include_runtime is true but no traces exist, including the _meta.runtime_caveat. It also mentions versioning (v1.10.0) and the top_n cap, providing rich contextual behavior.

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 two sentences, front-loaded with the core purpose, and every clause adds value. No fluff or repetition.

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 tool is read-only (as annotated) and there is no output schema, the description fully covers purpose, scoring inputs, edge-case behavior, and constraints. An agent can invoke it correctly without further documentation.

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 100%, so the baseline is 3. The description adds semantic context by explaining how include_runtime affects scoring and the caveat condition, which enriches the parameter's meaning beyond the schema's default value description.

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 uses a specific verb ('Return') and identifies the resource ('highest-risk columns in a dataset') with a clear composite scoring method. It distinguishes itself from sibling tools by focusing on risk ranking and the optional runtime traffic signal.

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 implies its use case ('highest-risk columns') but does not explicitly state when to prefer it over alternatives like get_dataset_health or data_health_radar. No exclusions or alternative tool references are provided, falling under 'implied usage'.

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