采样查看表数据

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

The annotations already establish that the tool is read-only, idempotent, and non-destructive. The description adds useful behavioral context: it avoids writing SELECT, handles identifier case/quoting concerns, and caps sampling at 5 rows by default to avoid filling the context.

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?

Three sentences with no filler: purpose comes first, usage guidance follows, and the rationale for the default limit is included. Every sentence 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?

For a simple read-only sampling tool with full parameter documentation and safety annotations, the description is nearly complete. It covers purpose, usage boundaries, and behavioral rationale. A small gap is that it does not describe the return format or acknowledge limit/offset semantics, though those are present in the schema.

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 description coverage is 100%, so the input schema already fully documents limit, offset, owner, table, and response_format. The description reinforces the default 5-row behavior and the table-oriented nature of the tool, but adds little parameter meaning 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 states a specific action: sampling rows from a specified table to inspect its data. It clearly differentiates this from dm_query by noting that filtering or aggregation belongs to that sibling tool.

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 explicitly says when to use this tool: when you want to quickly understand what data in a table looks like. It also gives an explicit exclusion: for conditional filtering or aggregation, use dm_query.

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