pydantic/pydantic-ai/mcp-run-python A MCP Server Active
Run Python code in a secure sandbox via MCP tool calls
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Run Python code in a secure sandbox via MCP tool calls
MCP server for the SLURM workload manager. Submit jobs, run YAML workflows, monitor GPU resources, manage SSH profiles, and sync files to remote HPC clusters from natural language. 14 tools spanning local SLURM and SSH-remote clusters; companion CLI and FastAPI Web UI ship in the same package.
Self-hostable GPU-aware job broker for your own machines. The MCP server exposes the queue as nine tools (submit, status, logs, list, cancel, preempt, workers, job-get, worker-delete) so an agent can dispatch long-running or GPU jobs that outlive the session, route them by VRAM tier, and babysit them across sessions. `pip install "jobd[mcp]"`.
Control Google Colab notebooks and assign GPUs (T4/L4/A100) from any AI agent. Enhanced fork of Google's colab-mcp with all tools visible at startup, OAuth GPU control, and Windows support.
Connect AI agents to MATLAB — execute code, run async jobs with progress reporting, get interactive Plotly plots, expose custom .m functions as tools, and monitor via live dashboard.
A Python Runtime Interpreter MCP Server that executes user-submitted code in an isolated environment.
SWI-Prolog execution for LLMs with CLP(FD), negation-as-failure, and recursion. Benchmarked 90% vs 73% LLM-only accuracy on 30 logic problems.
MCP server that lets LLMs execute code through the Piston remote code execution engine, with a zero-config `uv` setup and a ready-to-use Claude Desktop config example.
Give your AI assistant its own AI assistants. For example: "Could you ask openai to generate an image of a dog?"