execute_python

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

With no annotations provided, the description discloses captured stdout, pre-populated variables, and how to return data via a 'result' variable. However, it does not mention potential side effects, error handling, or sandboxing, leaving safety concerns unaddressed.

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 concise, front-loading the core purpose, listing pre-populated variables, and clearly separating the Arg section. Every sentence serves a purpose with no wasted words.

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 tool executing arbitrary code, the description covers return data and available variables but omits critical details like error handling, timeouts, or destructive potential. An output schema exists but is not shown; overall, the context is incomplete for safe usage.

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 sole parameter 'code' is described as 'Python code to execute,' adding slight meaning beyond the schema's type and title. With 0% schema description coverage, the description partially compensates but remains minimal.

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 tool executes Python code with Resolve API access, capturing stdout. The name and context distinguish it from the similar execute_lua sibling, though it does not explicitly differentiate when to use each.

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 provides no guidance on when to use this tool versus alternatives like execute_lua or other specialized tools in the sibling list. It lacks any 'when-not' or contextual usage advice.

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