profile_table

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

With no annotations provided, the description carries the full burden and does well by disclosing that all aggregation is performed in the database via SQL, no rows are transferred to local memory, and the output is a compact Markdown summary with specific token overhead. It does not mention failure modes or performance implications for very large tables, but it provides substantial behavioral detail beyond what the schema offers.

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 well-structured and front-loaded with the primary purpose, followed by a concise bulleted list of included statistics. Every sentence earns its place, and there is no redundant information or excessive length.

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 has only two simple parameters and an output schema exists (though not shown), the description covers the core behavior, execution model, and output format sufficiently. The only minor gap is the unspecified handling of categorical columns with >100 distinct values, but this is likely detailed in the output schema, and the overall description is complete enough for an agent to select and invoke the tool correctly.

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 schema already provides 100% coverage for both parameters, with clear descriptions for 'connection' and 'table_name'. The tool description adds no additional parameter-level detail about format, syntax, or constraints, so a baseline score of 3 is appropriate given the schema's high coverage.

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 opens with a specific verb+resource ('Generate a statistical profile of a database table') and clearly differentiates from sibling tools by detailing statistical aggregates (row count, nulls, min/max/avg, top-5 distribution) and output format (Markdown summary). This makes the tool's purpose unambiguous even among similar tools like describe_table and get_sample_data.

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 provides clear usage context, noting the output is 'suitable for AI context' and emphasizes the low token overhead, which implies when this tool is appropriate. However, it does not explicitly name alternatives or state when not to use this tool, so it lacks explicit exclusions but still conveys strong contextual guidance.

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