"Python" matching MCP servers:

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

MCP server for safely exposing SQL Server database capabilities to LLM clients, with read-only mode, security features, and observability.

MCP server for Python JSON logs

An MCP server that automatically instruments Python AI agents with the ioa-observe-sdk, adding OpenTelemetry-based tracing, metrics, and logs with zero manual effort.

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.

Record Python/Rust/TypeScript program runs to local SQLite; query call trees, exceptions, and value flow over MCP — refuses rather than guesses. Not the unrelated Telegram sensorium-mcp.

A JVM monitoring and control platform server based on Arthas that provides a Python interface for monitoring and analyzing Java processes with capabilities for thread analysis, memory monitoring, and performance diagnostics.

A Python-based MCP server that enables querying Cisco's Meraki Dashboard API to discover, monitor, and manage Meraki environments.

Runs, tests, and finds issues in your Python services with zero code changes, then helps your AI agent fix what breaks and proves it with acceptance tests.

Enables developers to build production-grade MCP services with dependency injection, zero global state, layered imports, and a sample user-notes tool, resource, and prompt. It includes Testcontainers, Prometheus metrics, Kubernetes probes, and structured logging for testable local and production deployments.

A live post-mortem debugger MCP server for Home Assistant that lets AI agents list captured exceptions, walk traceback frames, read local variables, and optionally evaluate Python in the context of a captured frame for true post-mortem debugging.

A Python utility for adding usage tracking, analytics, and audit trails to MCP servers using SQLite-backed persistence. It enables developers to monitor tool, prompt, and resource activity and expose these statistics directly to LLM clients.

Enables AI agents to trace Python code execution in real time via PEP 669, exposing call trees, variable deltas, and async context through MCP tools.

MCPWatch: observability for MCP servers. One line instruments any Python MCP server (FastMCP and the low-level Server) and tracks per-tool latency (p50/p95/p99), error rates, silent failures (empty/null returns and isError), and call volume. REST API, CLI, and alert hooks. MIT, no config required.

Detrix is an agentic debugger. Watch any variable at any line, zero code changes during debugging Local or cloud - same workflow for Docker containers and remote hosts Python, Go, Rust - observation points capture values without pausing, without restarting

Enables remote log searching, tailing, and file reading across servers via MCP tools, with pluggable execution via SSH, pyinfra, or local commands.

Enables AI assistants to diagnose a Python backend stack — Postgres, Celery, Redis and logs — in a single tool call, returning findings, a merged timeline of events, and possible cause-effect chains. All checks are strictly read-only, so an assistant can inspect blocked queries, missing workers, queue backlogs, Redis memory pressure and log error spikes without ever modifying anything.

MCP server for the FortiMonitor v2 monitoring API. 241 tools across 33 modules covering server management, outage monitoring, maintenance windows, metrics, notifications, cloud monitoring, SNMP, dashboards, reporting, and more. Unofficial community project, not affiliated with Fortinet. Python, MIT license, Docker and local deployment supported.

Python-based MCP server for full-cycle incident management, enabling detection, root-cause analysis, and response guidance through tools like detect_incidents, analyze_incident, and suggest_response.

An observability tool for agentic AI pipelines that intercepts MCP and Python tool calls to provide real-time metrics, session replays, and alerts via a local dashboard. It enables centralized monitoring of multiple MCP servers through multiplexer and proxy modes without requiring changes to existing agent code.