profile_model

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

Consistent with readOnlyHint=true, and it adds genuinely useful behavior beyond the annotation: output is aggregate-only with no raw rows or samples, and reads come from 'its current physical snapshot table' rather than live data. It also points to the README for known limits, though it doesn't disclose failure behavior when the model isn't applied or any cost/latency characteristics.

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 output content is front-loaded in the first sentence and the prerequisite is stated second, with no filler. Slightly long due to the parenthetical example and README pointer, but each element carries 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?

With no output schema, the description correctly spends its budget describing what comes back (aggregate stats, format signatures, no raw rows) and flags the applied-model requirement. A small gap remains around error/edge behavior and result structure, but for a single-parameter read tool this is close to sufficient.

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%, so the description must carry the single model_name parameter; it does add meaning by implying the name must refer to an already-applied model with a physical snapshot table. But it doesn't say how to obtain valid names (e.g., via list_models) or whether a qualified/unqualified name form is expected.

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?

States a specific verb and resource ('Aggregate-only profile of a model's columns') and enumerates the concrete outputs: null rates, distinct counts, numeric stats, and generalized string format signatures. It also explicitly distinguishes itself from row-returning siblings by stating 'Returns no raw rows or samples', which differentiates it from fetch_model_data without opening either schema.

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?

Provides a clear and non-obvious precondition ('The model must already be applied (see plan/apply_plan)') and routes the agent to plan/apply_plan for the prerequisite step. However, it never explicitly names an alternative for raw-row inspection (fetch_model_data) or states when-not to use this tool, leaving that comparison implicit.

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