exec_python

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

With no annotations provided, the description carries the full behavioral disclosure burden and does so thoroughly: it covers execution isolation, cost per run, no network access, read-only filesystem, timeout, and output cap. This is exemplary transparency for a code execution tool.

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?

One compact, front-loaded sentence conveys all critical constraints with no filler. Every clause adds meaningful information an agent needs before invoking the tool.

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 single-parameter tool with no output schema, this is nearly complete: it specifies environment, limits, cost, and constraints. The only minor gap is that it does not explicitly describe the return format (e.g., stdout/stderr), though the output cap strongly implies a returned payload.

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 schema already fully documents the sole parameter ('code'). The description adds execution-relevant constraints—timeout, output cap, network restrictions, filesystem restrictions—that inform how the agent should write code. This goes beyond the schema's basic type and description.

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 states the action ('Run Python code') and the execution environment ('isolated Docker sandbox'). It is immediately distinguishable from the sibling tool get_prediction_markets, which is about retrieving market data rather than executing code.

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 does not explicitly explain when to use this tool versus alternatives, nor does it state when not to use it. It implies use for arbitrary Python execution, but there is no routing guidance relative to get_prediction_markets or any other tool.

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