macro_usa_lmci

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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint), the description adds useful behavioral context: it specifies the data source URL, the date range (20141006-present), that the return type is a pandas.Series, and that the reported value is the current value in percent. This goes beyond what annotations provide and helps set expectations about output format and scope.

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, consisting of a title-like line, a URL, and docstring-style return annotations. It avoids unnecessary prose, but the structure is somewhat informal and mixes natural language with docstring syntax. Still, every sentence earns its place, and it is not overly verbose.

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 tool with no parameters and an output schema absent, the description provides essential context: the data source, date range, returned value, and return type. It does not explain the economic meaning of the index or reporting frequency, but given the simplicity of the tool and the clear name, this is adequate. The lack of an output schema makes the return description particularly valuable.

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?

There are zero parameters, and the schema coverage is 100% (trivially). The description adds meaningful information about what the tool returns (current value in percent, pandas.Series) even though it does not need to explain parameters. Given the absence of parameters, the baseline of 4 is appropriate.

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 specific indicator (美联储劳动力市场状况指数报告, i.e., Federal Reserve Labor Market Conditions Index Report) and includes the data range and source URL. While it lacks an explicit verb like 'returns' or 'fetches', the :return: field clarifies that it provides the current value (今值) of the index, making the purpose understandable and distinct from other macro_usa_* siblings.

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 no explicit guidance on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or alternatives among the many macro_usa_* tools. The data range and URL imply it is for retrieving US LMCI data, but this is not explicitly stated.

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