"How to run a Python script in a sandboxed environment" matching MCP servers:

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A Model Context Protocol (MCP) server for comprehensive monitoring and observability of systems using Langfuse.

An MCP server that provides cost and reliability observability for LLM and agent workflows. It records model calls and allows querying and aggregating telemetry data through MCP tools.

Enables AI agents to monitor Docker containers, system resources, ports, and logs in real-time.

Enables natural language queries for device support, including resolving device references, retrieving device details, historical metrics, and checking provisioning status. Maintains conversation context for multi-turn interactions.

Read-only MCP server for TrueNAS Scale debugging, providing k3s tools for kubectl operations and whitelisted midclt calls for system info, apps, and pools.

Enables MCP clients to triage SOC alerts while enforcing a trust firewall across retrieval, memory, and privileged actions. It exposes tools for alerts, logs, memory, and alert actions, with defenses that refuse risky operations in the presence of attacker-controllable content.

Enables real-time system monitoring and automation through MCP protocol with SSE transport, integrating with n8n workflows to check system health, query logs, and retrieve metrics from ABC system APIs. Supports natural language queries in Vietnamese and English for seamless system administration.

Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.

Enables analysis of AI agent traces to detect and fix failures using heuristic detectors, with no LLM calls required.

A locally-hosted MCP (Model Context Protocol) server that gives Claude real tools to debug Docker containers — fetch logs, inspect memory/CPU, and run diagnostic commands inside a container, all from a chat with Claude Desktop.

Enables AI assistants to manage remote servers via SSH with 14 commands for execution, file transfer, auditing, and monitoring.

A Python MCP server that reduces token usage by ~98% when working with log files by auto-detecting format and stripping noise to return only actionable signal.

A pre-action risk gate for AI agents. Your agent calls the forecast tool before any irreversible action — send email, run SQL, make a payment, delete a file — and gets a risk score (0–100) and a GO / CONFIRM / STOP verdict in a few seconds.

Enables LLM agents to run self-healing JMeter performance tests via natural language, from environment setup to capacity discovery and SLO verdict reporting.

A FastMCP server that provides LLMs with structured access to Scalene's CPU, GPU, and memory profiling for Python applications. It enables automated performance analysis, bottleneck identification, and optimization suggestions through natural language interactions in supported IDEs.

A unified CLI, web dashboard, and Python API for SLURM job management.

A Model Context Protocol (MCP) server for Chrome Enterprise Premium that exposes DLP rules, content detectors, connector policies, browser telemetry, and license management as MCP tools, enabling any MCP-compatible AI agent to inspect and configure a Chrome Enterprise environment.

Enables reading and analyzing Cloudflare Workers logpush data stored in R2 buckets. Supports searching logs with filters, viewing statistics, accessing errors, and browsing logs by date and environment.

Enables AI agents to manage a Canonical Landscape estate, including inventory, alerts, patching, and script execution, with built-in safety layers to prevent accidental destructive actions.