查看表结构

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

Annotations already declare readOnlyHint and idempotentHint, so safety is covered. The description adds valuable behavioral context: the optional DDL invocation and a DM-specific gotcha that index position columns are named COLUMN_POSITION while constraint position columns are named POSITION. This helps the agent correctly interpret results without needing to discover the quirk at runtime.

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 fluff: the first lists the complete output scope, the second gives the usage timing, and the third surfaces a critical naming distinction. Each sentence earns its place and the most important information is front-loaded.

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?

There is no output schema, so the description correctly takes on the burden of explaining return values, which it does thoroughly by enumerating all structural components. It also includes the optional DDL behavior and the crucial column-name discrepancy warning, making it complete for an agent to invoke and interpret results 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?

Schema description coverage is 100%, and the input schema fully describes all four parameters. The description adds only a small overlap by mentioning optional DDL, which duplicates include_ddl. Per the calibration baseline, 3 is appropriate when the schema does the heavy lifting.

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 and resource: '返回一张表的完整结构' and enumerates exactly what is returned (columns, keys, constraints, indexes, comments, optional DDL). It also distinguishes itself by explicitly stating it should be used before writing SQL to avoid guessing column names/types, clearly separating it from sibling tools like dm_query and dm_table_sample.

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 phrase '写 SQL 之前调用它，避免猜列名和类型' provides clear, explicit context for when to use this tool. However, it does not name alternatives or state when not to use it, so it falls short of a 5.

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