ask_with_code_execution

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

With no annotations provided, the description must fully disclose behavioral traits. It mentions Python REPL interaction but fails to describe execution details (e.g., sandbox, persistence, side effects, rate limits). The behavior of the code execution remains opaque.

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, front-loaded sentence with no wasted words. Every part contributes to explaining the tool's purpose and ideal use case.

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?

Given the tool has 5 parameters, no output schema, and no annotations, the description is insufficiently complete. It does not explain the purpose of key parameters like 'model' or 'mode', nor does it clarify what the tool returns after execution.

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?

Schema description coverage is 100%, so the baseline is 3. The tool description does not add meaning beyond the schema; it merely reiterates the code execution context without elaborating on how parameters like 'model' or 'mode' affect the tool's behavior.

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 uses a specific verb ('Ask') and specifies the resource ('questions that benefit from Python REPL interaction'). It clearly distinguishes from the sibling tool 'ask_with_realtime_information' by highlighting code execution capabilities.

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 states that the tool is for questions benefiting from Python REPL interaction, which gives clear context on when to use it. However, it does not explicitly explain when not to use it or directly compare with the sibling tool, missing exclusions.

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