Python REPL MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 |
|---|---|
| execute_pythonB | Execute Python code and return the output. Variables persist between executions. |
| list_variablesB | List all variables in the current session |
| install_packageB | Install a Python package using uv |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose with no overlap: execute_python runs code, install_package manages dependencies, and list_variables inspects the session state. An agent can easily tell them apart as they target different aspects of the Python REPL workflow.
All tools follow a consistent verb_noun pattern (execute_python, install_package, list_variables) with clear, descriptive names. The naming convention is uniform throughout the set, making it predictable and easy to understand.
With only 3 tools, the set feels thin for a Python REPL server, as it lacks operations like uninstalling packages, clearing variables, or handling errors. While the core functions are covered, the count is borderline low for the domain's typical scope.
The tools cover basic execution, package installation, and variable listing, but there are notable gaps: no way to update or remove packages, delete variables, or manage session state beyond listing. This could cause agent failures in more complex workflows.