index_qli_cx

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering the safety profile. The description adds that it returns a pandas.DataFrame and provides a source URL, but it does not disclose any potential behavioral traits such as data delay, column structure, or error conditions. It barely goes beyond 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 extremely short, which is not inherently bad, but it is somewhat unstructured—it reads as a title followed by a URL and return type. It is concise but not optimally organized, and it under-specifies the content of the returned DataFrame. It avoids unnecessary words, but the brevity borders on under-specification.

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, read-only tool, this description is minimally viable. It identifies the data source and return type, but does not describe what the DataFrame contains (e.g., columns, time range, or units). Since there is no output schema, the description should have provided more context about the actual data, but the simplicity of the tool and strong annotations partially compensate.

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 baseline is 4. There is no parameter information to add, and the description does not need to explain anything. The schema coverage is effectively complete, and the description adds no conflicting or missing parameter semantics.

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 '财新数据-指数报告-高质量因子' (Caixin Data - Index Report - High Quality Factor) and states it returns a DataFrame of '高质量因子'. This gives a specific resource and data type, but lacks a clear verb and does not distinguish it from sibling tools like index_ai_cx or index_si_cx. The URL provides a source, but the purpose is more of a label than a full explanation.

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 explicit guidance is provided on when to use this tool versus alternatives. The description only states the data source and return type; it does not mention exclusions, prerequisites, or context where other tools would be more appropriate. With no parameters, usage is trivial, but the description still fails to explain when to call it.

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