get_sample_data

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

With no annotations provided, the description carries the behavioral disclosure burden. It explicitly surfaces the non-obvious behavior that sensitive data is automatically masked, which is critical for an agent to know. It also implies a read-only sample-preview behavior, though it does not explicitly state side-effect safety or ordering/error 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 compact and front-loaded with the core action and masking caveat. The only minor weakness is a slight redundancy between '已自动脱敏' and the later sentence about sensitive fields being automatically masked.

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 three-parameter tool, the description covers the main purpose, example use cases, and the critical privacy behavior. It does not describe return shape or error cases, but this is not a serious gap given the simplicity of the operation and the schema's parameter coverage.

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 baseline is 3 and the schema already documents tableName, limit, and columns. The description does not add parameter-specific guidance beyond the general purpose of returning sample rows.

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 clearly states the specific action ('获取表的示例数据') and the intended purpose: understanding data formats such as date format, ID format, and amount precision. It distinguishes the tool as a row-preview operation, but it does not explicitly contrast it with sibling tools like get_table_info or generate_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 gives clear usage context: use it when you need to understand data format through actual sample rows. It provides concrete examples of what to look for. However, it does not state exclusions or name alternative tools, such as using get_table_info for schema-only details or execute_query for arbitrary queries.

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