stock_tfp_em

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

The annotations already declare read-only, idempotent, and non-destructive behavior. The description adds the return type (pandas.DataFrame) and the source URL, which provides some context but doesn't disclose details like potential network requirements or data freshness. This is adequate given the annotations.

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 compact docstring with labeled sections for URL, params, and returns. No unnecessary words, and the structure is clear and scannable.

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 simple data-fetching tool with one optional parameter and no output schema, the description provides the essential context: data source, input parameter, and return type. The only gap is the lack of usage guidance, but that is already covered in the usage_guidelines dimension. It is complete enough for an agent to invoke it correctly.

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 has a single optional parameter with 0% description coverage. The description compensates by documenting the parameter with an example value ('20240426') and labeling it as a query parameter. However, it does not explicitly specify the expected date format (YYYYMMDD), relying on the example to convey that.

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 sourcing East Money's data center suspension/resumption information and returns a pandas DataFrame. It clearly specifies the resource and data type, though it lacks an explicit action verb like 'get' or 'query'. The name also hints at the purpose, but the description makes it concrete.

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?

There is no guidance on when to use this tool versus other financial data tools, no prerequisites, and no mention of alternatives. The description simply documents the input and output.

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