"Resources for Learning Python Coding" matching MCP servers:

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

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

Enables LLMs to interact with Kibana through composable toolboxes for building dashboards, managing data, and handling alerts, among other operations.

Enables AI agents to interact seamlessly with Splunk environments through 20+ tools for search, analytics, data discovery, administration, and health monitoring. Features AI-powered troubleshooting workflows and supports multiple Splunk instances with production-ready security.

Enables AI-driven SOC investigations by providing automated Splunk querying, threat intelligence enrichment, and response actions through natural language. Includes tools for IP pivoting, lateral movement detection, and label harvesting.

Lets your coding agent check whether a production deploy actually worked. It waits for DeployAngel's verdict on a release (cleared, failed, or not cleared yet) and reads the evidence when one fails, including jobs that quietly stopped running.

A Model Context Protocol server that provides seamless integration with Coroot observability platform, enabling monitoring of applications, analysis of performance metrics, examination of logs and traces, and management of infrastructure through Coroot's comprehensive API.

An MCP server implementation that integrates AI assistants with Langfuse workspaces, allowing models to query LLM metrics by time range.

Provides a standardized way for MCP clients to interact with Apache Airflow's REST API, supporting operations like DAG management and monitoring Airflow system health.

MCP server that integrates with VictoriaMetrics vmanomaly API to enable AI-assisted anomaly detection, model management, and configuration generation.

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.

Enables AI clients like Claude to triage, investigate, and operate Icinga installations through natural language, integrating with Icinga's REST APIs and providing deep awareness of monitoring plugins and historical performance data.

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

A safety layer and monitoring copilot for AI agents using Splunk MCP, recording tool calls, enforcing policies, and blocking risky actions.

Integrates Monti APM with the Model Context Protocol to provide AI assistants with access to Meteor application performance monitoring data. It enables users to monitor system metrics, analyze method execution traces, and track application errors through natural language.

Provides Model Context Protocol access to the Dynatrace platform API, letting AI agents run live queries against observability, security, and platform data and manage a Dynatrace environment. It is hosted remotely by Dynatrace, so MCP hosts can add it by name from the official registry without installing anything.

A remote, Dynatrace-hosted MCP server that gives AI agents live API access to your Dynatrace environment. It enables querying and analyzing observability, security, and platform data — such as DQL queries, logs, traces, problems, and dashboards — so agents can act on production context instead of static knowledge.

MCP server for Python JSON logs

Enables provider-neutral policy enforcement, identity, persistent storage, and telemetry collection for reinforcement learning, evaluation, analytics, replay, and downstream dataset generation across standalone applications and agent systems.

Provides tools to search Splunk logs, inject test data, and send triage emails, enabling AI-driven incident investigation.

Enables read-only Kubernetes incident investigation through MCP tools for listing pods, describing resources, fetching logs, and searching runbooks.

An MCP server for querying Grafana Loki directly with a discovery-first workflow — labels, values, series, and LogQL queries without requiring Grafana.

Enables classification of GPU workloads as training, inference, or idle from telemetry data, with tools for one-shot classification, benchmarking, and audit log verification.

8 standalone MCP servers for SMBs — Guardrails (prompt injection, PII, secrets detection), FinOps (AI cost tracking and budgets), Observability (agent tracing), Smart Router (17+ models, 9 providers), Trust Score (governance scoring 0-100), Memory (persistent agent memory), ThinkSecure (runtime security + SOC2/GDPR audit), and A2A Lite (agent-to-agent protocol). 37 tools total, TNC credits

Web analytics and AI session replay for agents: visitors, breakdowns, funnels, goals, revenue attribution, and AI-found site issues. Connects to the hosted Flowsery MCP server.

16-tool MCP server for Google PageSpeed Insights & Chrome UX Report APIs. Analyze, compare, and optimize web performance directly through Claude, Cursor, or any MCP-compatible AI client.

Model Context Protocol (MCP) server for debugging, analyzing, and diagnosing Kubernetes clusters directly from AI agents.

Unified observability gateway for AI agents — one MCP server for Prometheus, Loki, and any backend.

Read-only MCP server for Grafana Loki: lets AI agents (Claude Code, Cursor, Copilot) discover labels, read bounded LogQL pages, count lines by label or time, and export logs — across several Loki stands

MCP server exposing rlg (RustLogs) log files as tools for on-call / SRE agent workflows. Three tools over JSON-RPC stdio — tail_log (last N events, filtered by level/target), filter_log (predicate filtering), summarize_errors (grouped error taxonomy). Pure Rust. MIT OR Apache-2.0.