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luutuankiet

MCP Python Interpreter

by luutuankiet

run_python_code

Run Python code in a chosen environment, return the output, and optionally save the code as a file.

Instructions

Execute Python code and return the result. Code runs in the working directory.

Args:
    code: Python code to execute
    environment: Name of the Python environment to use (default if custom path provided, otherwise system)
    save_as: Optional filename to save the code before execution (useful for future reference)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes
environmentNodefault
save_asNo
Behavior2/5

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

With no annotations, the description carries full burden. It mentions 'Code runs in the working directory' but does not disclose potential side effects (e.g., file modification), security implications, or what 'return the result' means (stdout/stderr/return value). Minimal behavioral disclosure for an 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and front-loaded with the main action. Parameter descriptions are in a clear list. No unnecessary words. However, a slightly longer description with behavioral details would not harm conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and moderate complexity, the description lacks details on return values, error handling, security, and alternatives (vs run_python_file). Not complete for an execution tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so description must compensate. It adds context for each parameter: code (the code to execute), environment ('Name of the Python environment...'), and save_as (optional filename). This adds value beyond the schema, but details are vague (e.g., 'custom path provided' unspecified, 'system' environment unclear).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Execute Python code and return the result' with a specific verb+resource. It also mentions execution context ('working directory'). However, it does not explicitly differentiate from sibling tools like run_python_file, so it's not a perfect 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. No context about prerequisites, typical use cases, or exclusions. The description only explains the parameters, not usage strategy.

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

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