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MCP Python Interpreter

MCP Python Interpreter

by yzfly
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

run_python_file

Execute Python files with specified environments, arguments, and timeout settings using subprocess execution.

Instructions

Execute a Python file (always uses subprocess for file execution).

Args:
    file_path: Path to the Python file to execute
    environment: Name of the Python environment to use
    arguments: List of command-line arguments to pass to the script
    timeout: Maximum execution time in seconds (default: 300)

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "file_path": "<file_path>"
    },
    "name": "run_python_file"
  }
}

Input Schema

NameRequiredDescriptionDefault
timeoutNo
argumentsNo
file_pathYes
environmentNodefault

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.0.0
    • addedInput schema / properties / timeout
      Added value: +{
+  "default": 300,
+  "title": "Timeout",
+  "type": "integer"
+}
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "result": {
+      "title": "Result",
+      "type": "string"
+    }
+  },
+  "required": [
+    "result"
+  ],
+  "title": "run_python_fileOutput",
+  "type": "object"
+}
  2. First observed

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It discloses the execution method (subprocess) and mentions a default timeout, but doesn't cover critical behaviors like error handling (e.g., what happens if the file doesn't exist or execution fails), output capture (e.g., stdout/stderr return), security implications, or resource usage. This is inadequate for a tool that executes arbitrary code.

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 well-structured with a clear purpose statement followed by a bullet-point-like Args section. Each sentence adds value, and there's no redundancy. However, the Args formatting could be more integrated, and it's slightly verbose for a tool with only 4 parameters.

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

Completeness3/5

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

Given the complexity (executing arbitrary Python files), lack of annotations, and presence of an output schema, the description is moderately complete. It covers parameters adequately but lacks behavioral details like error handling or security warnings. The output schema may help with return values, but the description doesn't reference it, leaving gaps in understanding the tool's full impact.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate. It adds meaningful context for all 4 parameters: file_path specifies it's the 'Python file to execute,' environment indicates the 'Python environment to use,' arguments are 'command-line arguments to pass to the script,' and timeout defines 'maximum execution time in seconds' with a default. This goes beyond the schema's basic titles, though it could elaborate on format (e.g., path requirements).

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 the tool's purpose as 'Execute a Python file' with the specific implementation detail 'always uses subprocess for file execution.' This distinguishes it from sibling tools like run_python_code (which executes code directly) and read_file (which only reads). However, it doesn't explicitly contrast with all siblings like install_package or list_directory.

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

Usage Guidelines3/5

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

The description implies usage by specifying it executes files via subprocess, suggesting it's for running complete Python scripts rather than inline code. However, it lacks explicit guidance on when to use this versus alternatives like run_python_code, and doesn't mention prerequisites (e.g., file must exist) or exclusions (e.g., not for interactive scripts).

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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