stock_board_concept_name_em

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds the return type (pandas.DataFrame) and a source URL, but does not disclose additional behavioral traits like data freshness, column structure, or error behavior. With annotations present, the incremental value is modest.

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 three concise lines: a Chinese title, a reference URL, and a return type. It is front-loaded with the source and purpose, with no wasted words.

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 no-parameter tool without an output schema, the description provides the essential return information: a pandas DataFrame of concept board names. It does not list columns or indicate total count, but it is adequate for a basic list retrieval.

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 tool has zero parameters, and the schema fully covers this (100% coverage). Per rubric, a parameterless tool receives a baseline of 4; the description adds no parameter semantics because none exist.

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 identifies the tool as returning concept board names from East Money's market center, specifying the source and board type. It distinguishes from siblings like stock_board_industry_name_em by the '概念板块' (concept board) keyword, though the verb ('list', 'retrieve') is only implied.

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?

No guidance is provided on when to use this tool versus alternatives, nor any context such as prerequisites or typical use cases. It is a self-contained no-parameter fetch, but the description does not state scenarios or exclusions.

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