clo_run_python

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

No annotations exist, so the description must carry the behavioral burden. It usefully discloses pre-imported modules, stdout capture, coordinate conventions, and return-value semantics. However, it omits significant safety context: running arbitrary Python can mutate the CLO document, create/delete objects, or perform destructive actions, which is critical for an agent to understand before invocation.

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?

Four concise, information-dense sentences with no filler. The most important facts are front-loaded, and the example concretely anchors the usage. Every sentence earns its place.

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 powerful code-execution tool with no output schema and no annotations, the description covers core mechanics well: modules, return conventions, stdout, and units. But it lacks explicit warnings about side effects on the current scene, persistent state changes, or error behavior, which are important for safe and correct use in an autonomous agent context.

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 0%, so the description must compensate. It adds meaning to `code` by explaining expression vs. assignment semantics, stdout capture, and the example pattern. `timeout_seconds` is not addressed, though its name and default make it mostly self-explanatory. The compensation is partial but adequate.

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?

States a specific verb and resource: 'Run Python inside CLO with the CLO API modules pre-imported.' The list of modules and the concrete example make the resource unambiguous. Among sibling tools, it is clearly distinct as the generic Python execution entry point.

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 explains how to write code (expression vs. assignment to `result`) but gives no guidance on when to prefer this tool over siblings like clo_api_call, clo_api_search, or clo_status. No exclusions or alternative routing are provided.

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