forex_spot_em

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety. The description adds the data source (Eastmoney) and return type (pandas.DataFrame), plus the scope ('all rates'), which is useful context. However, it doesn't disclose any additional behavioral traits like pagination, update frequency, or potential issues, but with annotations present, the bar is lower.

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 a single string containing the title, source URL, and return annotations. It is relatively concise and front-loaded with the key purpose. The inclusion of the URL and return type is useful, though the format is slightly unstructured. No unnecessary words, but it could be organized better.

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?

With no parameters and no output schema, the description should ideally explain what the returned DataFrame contains (e.g., columns like currency pair, price, change). It only says 'real-time market data' and the return type. This is adequate for a simple spot data tool but lacks detail about the returned fields, which could matter for an agent deciding how to use the data.

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 no parameters, so the schema is trivially covered. The description still provides context about the return content (real-time market data) and type (pandas.DataFrame), which adds meaning beyond the empty schema. For a zero-parameter tool, this is sufficient.

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 providing real-time spot data for all foreign exchange rates from Eastmoney.com, with a specific URL. It uses a specific verb ('实时行情数据' = real-time market data) and resource. While it doesn't explicitly differentiate from sibling tools like forex_hist_em, the 'spot' and 'all rates' scope makes the purpose reasonably clear.

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 implies usage for retrieving real-time snapshot data of all forex rates from Eastmoney, but it provides no explicit guidance on when to use this tool versus alternatives (e.g., for historical data or specific currency pairs). No exclusions or alternative tool mentions are given, so usage guidance is only implicit.

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