run_code

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

With no annotations, the description carries the full burden and discloses key traits: persistent session, state preservation, pre-imported symbols, and return behavior (stdout, last-expression echo, or traceback). This is substantial coverage 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?

Two sentences, front-loaded with the primary purpose. Every element earns its place: what it runs, persistence, pre-imports, and return behavior. No redundancy or filler.

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 is simple (1 required param) and the description covers purpose, statefulness, available symbols, and return values. The presence of an output schema further clarifies the return structure, making this description adequate on its own.

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 single 'code' parameter is explained as a cell of Python/PySpark, and the pre-imports listed clarify what is available in scope. This adds meaning beyond the raw schema ('code' string) and compensates for the 0% schema description coverage.

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 ('Run') and resource ('a cell of Python/PySpark against the persistent session'), clearly distinguishing it from sibling tools like run_sql. It states exactly what the tool does without ambiguity.

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 clear context: it is for Python/PySpark cells, state persists across calls, and pre-imports are listed. It implicitly distinguishes from SQL execution (run_sql) but does not explicitly state when not to use or mention alternatives.

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