Execute

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

With no annotations, the description carries the burden of explaining behavior. It usefully discloses that only `call_tool(tool_name: str, params: dict) -> Any` is in scope and that output is produced with `return`. However, it does not describe execution environment constraints, error behavior, side effects, or persistence across blocks, leaving meaningful gaps.

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 three short sentences with no filler. The core instruction is front-loaded, and every sentence contributes either to usage, output, or scope constraints.

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, the description is largely sufficient: it explains how to invoke tools, how to return output, and what is available in scope. It could be more complete by noting error/exception behavior or execution restrictions, but these are not critical for basic 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 input schema already documents `code` at 100% coverage, but the description adds real value by specifying the callable signature, the need for `await`, and the output mechanism. This goes beyond merely restating the schema.

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 that the tool executes a Python block which chains `await call_tool(...)` calls and returns output via `return`. It is not a tautology and gives a concrete sense of the tool's role, though it does not explicitly differentiate itself from siblings like `search` or `get_schema`.

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?

It gives clear context for when to use the tool: when multiple tool calls need to be chained in one Python block, and it advises preferring a single block and using `return`. It lacks explicit exclusion criteria or named alternatives, but the usage intent is clear.

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