"Automating Business Processes in Marketing" matching MCP servers:

Enterprise-grade MCP server with multi-agent system for business AI transformation across finance, healthcare, retail, and other domains. Provides specialized AI agents for data analysis, API execution, business validation, and report generation with real-time monitoring and observability.

Lets an assistant read recorded production AI agent runs — steps, model and tool calls with arguments, and outcomes — along with the findings of comparison runs that checked a prompt or model change against those recordings, and verify a signed evidence bundle offline with no account or network. It is strictly read-only: no tool starts a replay or comparison, so nothing can spend money or act without a person deciding.

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.

MCP server providing 75 tools for querying devices, managing alerts, running scripts, creating tickets, and more via the NinjaOne RMM platform.

Read-only AMD GPU monitoring for Claude on Linux — VRAM, temperature, clocks, power, and running ML processes via rocm-smi.

Enables Claude to monitor CPU, memory, disk, processes, network connections, Docker containers, and system logs for system diagnostics and troubleshooting.

AI-native developer observability MCP (Model Context Protocol) server that gives AI agents full-stack visibility into your development environment — browser, terminal, and processes — all in one unified interface.

A real-time system diagnostics MCP server that gives AI agents live access to CPU, RAM, disk, network, processes, and hardware health metrics, with zero cloud dependency.

MCP server that exposes live host metrics (CPU, memory, processes, network) to Cursor/Claude via /proc and optional eBPF, enabling system monitoring dashboards and natural language queries.

Lets an AI assistant inspect a Linux machine through ten fixed read-only tools covering load, memory, disks, processes, ports, interfaces, systemd units, the journal, and allowlisted log files. All output is size-capped, control-character-stripped and secret-redacted, and nothing in the package can write to the system.

A framework for building MCP servers with built-in enterprise capabilities such as authentication, authorization, telemetry, audit logging, and data classification, allowing domain developers to focus on business logic while maintaining governance and compliance.

Aggregates logs from multiple processes and provides an MCP server for AI agents to access, search, and manage logs in real-time.

Enables AI assistants to interact with DataDog's observability platform through a standardized interface. Supports monitoring infrastructure, managing events, analyzing logs and metrics, and automating operations like alerts and downtimes.

Enables LLMs to monitor system health, manage processes, execute shell commands, and inspect environment variables and directories.

Multi-machine system monitor with a built-in MCP server that enables AI agents to query health metrics, manage processes, schedule cron jobs, and run diagnostics across local and remote machines.

This server provides real-time system performance metrics via Performance Co-Pilot (PCP), enabling users to monitor CPU, memory, disk, network, processes and more through natural language queries.

Provides system diagnostic capabilities including CPU, memory, disk usage, top processes, and port status checking.

A natural language-based server analysis and monitoring system that automatically processes user queries about server status and provides detailed responses with visualizations.

MCP-native agent evaluation and observability server. Log traces, evaluate output quality with 12 built-in rules (PII detection, prompt injection, cost thresholds), and track agent costs. Real-time dashboard, OTel-compatible spans. Self-hosted, MIT licensed.

Tells an agent what went wrong with its own Ollama calls, in sentences with the fix: lost prompt cache and where the prompt stopped matching, context overflow, a model running partly on CPU, tool schemas crowding the prompt. Also the last turn in numbers and which local models fit in video memory. Local, read-only, nothing leaves the machine.