"Using Cline as a Knowledge Graph for Coding" matching MCP servers:

Enables read-only, cited lookups of official Zendesk content across the Help Center, developer/API docs, changelogs and release notes, and live service status and maintenance. It also reports whether a feature is current, beta, EAP, deprecated, or retired.

A Model Context Protocol server for querying and analyzing Azure resources at scale using Azure Resource Graph, enabling AI assistants to explore and monitor Azure infrastructure.

Shows your current Claude Desktop activity as Discord Rich Presence, including model, status, usage, and subscription info.

Exposes a CognOS agent system as a machine-readable JSON graph, enabling Claude to get a full system snapshot and inspect node status, edges, and blocked nodes.

Enables AI assistants to monitor and control Bambu Lab 3D printers over a local network via MQTT, including real-time status, AMS filament info, print control, and maintenance tracking. It supports guided setup, safety confirmations, read-only mode, and a built-in printer simulator.

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

An unofficial MCP server that connects AI assistants to Akamai APIs, providing tools for edge diagnostics, debugging, and network analysis.

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 an AI client to work with the desktop-selected process’s typed memory searches, refinements, live watches, snapshot comparisons, progress, and cancellation through a local stdio MCP connection.

Enables Claude to read a project's FastStats metrics and then design, validate against real data, and publish charts, dashboards, event markers, and custom data sources. Every write is previewed and dry-run first, with read-only mode and confirmation guards for destructive actions.

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 AI assistants to explore a PostgreSQL database, run bounded read-only SQL, inspect query plans, test hypothetical indexes, find slow queries, and check database health with compact CSV output.

Enables AI apps to ask what WSJT-X (and compatible JT-mode programs) is hearing by listening to its UDP feed read-only: who is calling CQ and what kind, recent decodes, stations heard, WSPR spots, and current dial frequency, mode and Tx status. It never transmits or changes settings, so it cannot answer a call, key a transmitter, or alter a configuration.

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

Connects AI assistants to xyOps through Streamable HTTP, exposing tools for events, jobs, tickets, alert invocations, monitoring snapshots, and servers with API-key-scoped access.

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

Bridges AI models with WinDbg to analyze Windows crash dumps and perform remote debugging through natural language queries, enabling execution of debugger commands and automated crash analysis.

A standalone Python/FastAPI server that implements the Model Context Protocol (MCP) for the OPTIX threat intelligence platform. It exposes 26 analyst-friendly tools that AI assistants and programmatic consumers can use to query threat feeds, search documents and indicators, manage watchlists, triage IOCs, generate detection rules, trigger AI research, and produce intelligence reports.

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

Brings live project context into Slack via MCP, enabling developer teams to check service health, recall team decisions, search code, and query project context directly from chat.

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 local computer diagnostics by sampling CPU, memory, disk, and network activity and displaying the busiest processes in an interactive dashboard. It also supports human-confirmed process-stop proposals while keeping diagnostics local and bounded.

Provides an interface to access Google Analytics Data API through Model Context Protocol (MCP), allowing users to retrieve reports and realtime data from Google Analytics 4 properties.

Enables querying an F5 BIG-IP lab through read-only iControl REST calls, letting Claude inspect system and HA status, virtual servers, pool member health, monitors, and other objects via natural language. All tools are GET-only and paired with a read-only BIG-IP user role so nothing on the device can be changed.

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.

Enables EVTX (Windows Event Log) analysis via Hayabusa, providing tools to scan event logs and retrieve detection rules.

A remote MCP server that provides AI agents access to the Rootly API for incident management, allowing users to query and manage incidents, alerts, teams, services, and other incident management resources through natural language.

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.

Enables searching Sumo Logic logs using the search_logs tool, with support for query parameters such as time range and maximum results.

Acts as a production-grade safety layer for AI-assisted coding, monitoring Git hygiene, scanning for security issues (PII, secrets, injection), and enabling semantic history search.

Provides health checks, binary validation, and auto-repair for MCP configurations in Claude Desktop, Cursor, and Cline.

MCP Server for notify to telegram / weixin

Discord server management MCP for AI agents: chat, moderation, admin, and full server building. 160+ tools, confirmations the model cannot answer, undo. Formerly Omnicord.

MCP server for Yandex Webmaster API v4.1 with 37 tools covering indexing, search queries, diagnostics, recrawl, sitemaps, links, and important URL monitoring.

Allows AI assistants such as Cursor/Cline/GitHub Copilot to use Google's lighthouse tool to measure perf metrics for your webpage. You can then run an agentic loop and get the assistants to optimize those metrics!

Connects AI coding assistants to Shipbook for investigating issues using real application logs with error classification, log search, and session tracing.

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.

Maple is a unified MCP server for agent observability, safety control, and behavior evolution, acting as a monitoring and auditing layer for high-agency agents. It enables developers to capture session timelines, replay risky branches, detect anomalies using ML, and enforce action guardrails.