Python Sandbox
This MCP server provides secure Python code execution capabilities through a containerized sandbox environment optimized for data science and machine learning workflows. Built by aamir-gmail specifically for LM Studio integration, it features automatic matplotlib figure persistence, per-execution isolation with unique run directories, and dual deployment modes supporting both LM Studio's native MCP chat interface and a Gradio web UI through the developer API. The implementation includes a comprehensive data science stack (numpy, pandas, scikit-learn, matplotlib, seaborn, xgboost) with automatic artifact serving, making it valuable for interactive data analysis, visualization generation, and machine learning experimentation where users need reliable code execution with persistent file outputs accessible via web URLs.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-19.
Scan details: Circle-IR · 2026-09-19 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- devops-ci
- Source
- PulseMCP
- Repository
- github.com/aamir-gmail/lm_studio_mcp
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve Python Sandbox from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.