stock_register_bj

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the source URL (https://data.eastmoney.com/xg/ipo/) and the return type (pandas.DataFrame), which is useful context beyond annotations. However, it does not disclose any additional behavioral traits such as data freshness, network requirements, or potential errors, which is acceptable for a simple data-fetch tool but not exceptional.

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, consisting of four lines with no filler words. It front-loads the source and topic, followed by URL and return type. The only minor issue is the ':return: 北交所' line, which is vague and could be misinterpreted, but overall it is well-structured and efficient.

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?

The tool has no parameters and no output schema, so the description must carry the burden of explaining what is returned. It states the return type (pandas.DataFrame) and the topic (IPO review info), but the return value line '北交所' is unhelpful and does not enumerate columns or content details. Given the simplicity of a zero-param fetch, the description is adequate but leaves gaps about the actual data structure, which warrants a 3 rather than a 4.

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, so the schema fully covers all parameters (trivially). Per the rubric, 0 params gets a baseline of 4. The description adds nothing about parameters, but none are needed.

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 identifies the tool as '东方财富网-数据中心-新股数据-IPO审核信息-北交所' (Eastmoney Data Center - IPO Review Info - Beijing Stock Exchange), which clearly specifies the data source and target market. The tool name 'stock_register_bj' further reinforces the Beijing exchange focus, distinguishing it from sibling register tools. However, it lacks an explicit verb like 'get' or 'list', relying on the implied DataFrame return type to convey retrieval.

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 clearly implies usage for Beijing Stock Exchange IPO review data via the '北交所' label, and the sibling tools (stock_register_cyb, stock_register_kcb, etc.) suggest other markets. This provides clear context about when to use this tool, but it does not explicitly state alternatives or exclusions, which would earn a 5.

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