MCP Python Interpreter
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| MCP_ALLOW_SYSTEM_ACCESS | No | Controls whether the MCP server has access to system resources | 0 |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| read_fileA | |
| write_fileB | |
| list_directoryA | |
| list_python_environmentsB | List all available Python environments (system Python and conda environments). |
| list_installed_packagesA | |
| run_python_codeB | |
| install_packageA | |
| write_python_fileB | |
| run_python_fileB | |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| python_function_template | Generate a template for a Python function with docstring. |
| refactor_python_code | Help refactor Python code for better readability and performance. |
| debug_python_error | Help debug a Python error. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| get_environments_resource | List all available Python environments as a resource. |
| get_file_in_current_dir | List Python files in the current working directory. |
| get_working_directory_listing | List all Python files in the working directory as a resource. |
TDQS
Scored across 9 tools
Most tools have distinct purposes, but there is some overlap between write_file and write_python_file, as both handle file writing with similar arguments, which could cause confusion. However, descriptions clarify that write_python_file is specifically for Python files, reducing ambiguity.
All tool names follow a consistent verb_noun pattern (e.g., install_package, list_directory, run_python_code), with clear and predictable naming throughout the set. No deviations or mixed conventions are present.
With 9 tools, the count is well-scoped for a Python interpreter server, covering package management, file operations, code execution, and environment handling. Each tool serves a clear purpose without being excessive or insufficient.
The toolset provides comprehensive coverage for core Python interpreter tasks, including installation, execution, and file management. Minor gaps exist, such as no tool for deleting files or packages, but agents can work around this using existing tools like write_file with overwrite or system commands.