Python Executor 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
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| run_pythonA | Execute a single Python code snippet. Use for stateful/dependent operations. Use run_python_batch for parallel independent tasks. Max 1MB code, 60s timeout. See README.md for usage examples and helper utilities. |
| run_python_batchA | Execute multiple Python snippets in parallel (max 4 workers, max 20 snippets). Use for independent tasks: file processing, parallel data analysis. Use run_python for stateful/dependent operations. See README.md for examples. |
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 2 tools
The two tools are clearly distinct: run_python for a single snippet and run_python_batch for parallel execution. Descriptions explicitly state when to use each, eliminating ambiguity.
Both tools follow a consistent pattern: 'run_python' and 'run_python_batch', with the second adding a descriptive qualifier. This is predictable and easy to understand.
With only two tools, the server feels minimal. The tools cover the core need, but the count is borderline per calibration standards (1-2 tools is considered thin).
For a Python execution server, single and batch execution cover the primary use cases. Minor gaps exist (e.g., no explicit environment management), but the core functionality is well-covered.