run_python

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

No annotations are provided, so the description fully carries the behavioral disclosure burden. It reveals key behaviors: default 10-second wait timeout, the use of # loommux: directives, handling of full output and truncation, image display, execution ID assignment, and error responses for invalid directives. It is comprehensive and transparent.

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 well-structured with clearly labeled sections (输入, 等待上限, 完整输出, etc.), front-loaded with the purpose, and every sentence contributes necessary information. It is appropriately sized for the complexity of the tool, with 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 the moderate complexity (1 parameter, no output schema), the description covers input format, directives, default behaviors, error handling, follow-up tools, and output truncation. It provides a complete understanding of how the tool functions and what to expect, making it self-sufficient for an AI agent.

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?

There is only one parameter 'freeform' with 100% schema description coverage, establishing a baseline of 3. The tool description adds significant value beyond the schema by explaining how to embed # loommux: directives for controlling wait and output behavior, thus enriching the meaning of the parameter.

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 starts with a clear statement: '向持久 IPython kernel 提交一个原始 Python cell.' It specifies the verb (submit) and resource (raw Python cell to persistent IPython kernel), distinguishing it from siblings like wait_python or read_python_output which handle follow-up actions.

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 explicitly tells when to use sibling tools: '使用 wait_python 等待，使用 python_execution_status 查看状态，使用 read_python_output 或 search_python_output 读取或搜索保留的输出.' It does not mention interrupt_python as an alternative, which is a minor gap, but overall provides clear context on when to use this tool vs others.

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