python_execution_status

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

With no annotations, the description carries full burden. It discloses that the tool does not return full output, describes default selection logic, and mentions the `execution_not_found` error. It does not cover side effects or authentication, but for a read-only status check this is acceptable.

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 efficient: a one-sentence purpose followed by a clear bullet list of selection rules. Every sentence adds value, no wasted text.

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?

Given no output schema, the description explains the return type (status & metadata, not full output) and selection logic. It could list specific status fields or clarify differences from `python_status`, but for a focused tool it is largely complete.

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 coverage is 100%, so baseline is 3. The description adds value by explaining the default selection rules (running, then recently accepted) and the error case, going beyond the schema's simple 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 that the tool returns execution status and metadata, not the full output. This distinguishes it from output reading tools like `read_python_output`. The verb '返回' and resource 'execution 的状态与元数据' are precise.

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 explicit selection rules for the `execution` parameter, detailing when to provide it and the default behavior (running, then recently accepted). However, it does not contrast with the similar sibling `python_status`, so tool-level guidance is slightly lacking.

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