get_info

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

No annotations are provided, so the description carries the full burden. It conveys a read-only operation by saying 'Get' and 'Check' and compares output to pandas df.info(). However, it does not explain what happens when df_name is empty or invalid, how the target dataframe is selected, or whether it can raise errors. The behavior is adequately implied for a simple info tool but has clear gaps.

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 short and front-loaded with the core purpose. However, the sentence 'Check column types and non-null counts' is largely redundant with the first sentence, so there is minor waste. Still, the overall length is appropriate and the key information is easy to scan.

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 only one parameter and an output schema, so the description does not need to explain return values. It covers purpose and a usage trigger. Yet the df_name parameter is left unexplained, and the 'for the dataframe' phrasing is ambiguous, so the description is not fully complete for an agent to use it correctly without guessing.

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 is only one parameter, df_name, and the schema description coverage is 0%. The description never mentions df_name, how it is used, what values it accepts, or what happens when it is left as the default empty string. The phrase 'for the dataframe' is too vague to compensate for the schema gap.

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 opens with a specific verb ('Get') and resource ('the dataframe'), listing exact outputs: column dtypes, non-null counts, and memory usage. It also adds the 'Similar to pandas df.info() output' analogy and a concrete use case ('verify names'), which clearly differentiates it from siblings like get_statistics or get_head.

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 gives a clear, explicit trigger: 'Run when a tool reports 'column not found' to verify names.' It also describes the purpose (checking column types and non-null counts). It does not mention when not to use it or name alternatives, so it falls short of a full 5.

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