lookup_table_columns

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

With no annotations provided, the description carries the full burden. It discloses that the tool performs a read-only lookup of column definitions and, via the Chinese text, specifies the output fields. It does not mention any limitations or side effects, but for a simple metadata lookup, this is adequate.

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 concise and well-structured: the core purpose is stated upfront, followed by a usage hint and a clearly formatted Args section. The bilingual line (English + Chinese) adds value by clarifying what 'definitions' means, and no extraneous content is present.

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 single-parameter tool with an output schema, the description is complete. It explains the tool's purpose, provides a usage scenario, and documents the parameter with examples. There is no need to explain return values since an output schema exists.

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 provides only a string parameter with no description (0% coverage). The description compensates by explicitly documenting the parameter: 'table_name: Table name (e.g. 'balance_sheet', 'income_statement')', including concrete examples, which is highly useful for an agent.

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 tool 'looks up all column definitions for a JoinQuant data table', specifying both the action and resource. It also includes a Chinese translation that enumerates the returned fields (name, type, meaning), distinguishing it from sibling tools that handle functions or documentation.

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 a concrete use case: 'Critical for get_fundamentals queries where exact column names are needed.' This tells the agent when to use the tool, but it does not explicitly mention alternatives or 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.