get_table_stats

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

The description specifies what is computed but omits behavioral traits such as being read-only, potential performance impact (e.g., table scan), or required permissions. With no annotations provided, the description should disclose these to fill the gap.

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, efficient sentence that conveys the tool's purpose and outputs without any extraneous information. Every word earns its place.

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 a single parameter and no output schema, the description adequately states the outputs. However, it does not mention the return format or prerequisites (e.g., table must exist, a database connection must be active), which would improve completeness.

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 'table_name' with a basic description. The tool description adds context by linking the parameter to the profiling action, but since schema coverage is 100%, baseline is 3. The description adds marginal value beyond the schema.

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 ('Profile') and resource ('a table') and explicitly lists the computed statistics (row count, null rates, cardinality, numeric ranges), making the tool's purpose clear and distinct from siblings like describe_table or run_query.

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 explicit guidance on when to use this tool versus alternatives, but the implied context (profiling table statistics) differentiates it from siblings. The description could benefit from stating that it is for summary statistics, not for schema or queries.

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