MCP 服务器

Apache SkyWalking's MCP server gives agents access to observability data for tracing, service topology, metrics, logs, and production incident investigation.

Buildkite's official MCP server for querying and operating CI/CD pipelines, builds, jobs, artifacts, and organization-level delivery workflows.

Dagster's MCP integration enables agents to inspect and operate data assets, jobs, schedules, and runs in modern data platforms.

Harness' official MCP server for connecting agents to software delivery workflows, including deployment pipelines, services, environments, and delivery insights.

JFrog's MCP server lets agents work with Platform services for artifact repositories, build information, release lifecycle management, and software supply-chain workflows.

Octopus Deploy's official MCP server helps agents inspect, query, and diagnose deployment infrastructure, projects, releases, and environments.

Prefect's MCP integration gives agents access to orchestration objects including flows, deployments, work pools, and run diagnostics.

Microsoft's official MCP server for Azure DevOps. Brings Azure DevOps capabilities to AI agents, including managing work items and boards, browsing Git repositories and pull requests, querying builds and pipelines, and accessing wikis and test plans. Lets teams drive their Azure DevOps workflows through natural language from MCP-compatible tools.

Confluent's open-source MCP server that connects AI assistants to Confluent Cloud, Confluent Platform, and standalone Apache Kafka deployments. Provides tools to manage Kafka topics and connectors, work with Schema Registry, and run Flink SQL statements through natural language, helping teams operate streaming data platforms from an MCP client.

Dynatrace's official MCP server that brings the Dynatrace observability platform into AI workflows. Lets assistants query problems and vulnerabilities, run DQL against logs, metrics, and traces, inspect entities, and pull real-time monitoring data directly into a developer's coding environment for faster troubleshooting and root-cause analysis.

Honeycomb's MCP server that lets AI assistants query and analyze observability data, including events, traces, alerts (triggers), and boards. Agents can run queries against datasets, inspect columns and schemas, and cross-reference production behavior with the codebase to investigate incidents. Connects to Honeycomb via API key or OAuth.

MCP server that lets AI assistants query and analyze Prometheus metrics through standardized interfaces. Exposes instant and range PromQL queries, metric and label discovery, and target/health inspection, allowing agents to investigate system performance and troubleshoot incidents using natural language instead of hand-writing PromQL.

Pulumi's official MCP server for AI-assisted Infrastructure as Code. Wraps the Pulumi Automation and Cloud APIs so agents can preview and deploy stacks, read stack outputs, inspect resources, and look up provider/resource schemas from the Pulumi Registry. Helps developers codify cloud architectures and review infrastructure diffs from within an AI coding assistant.

Official CircleCI MCP server. Lets AI agents fetch build and pipeline status, retrieve failed build logs, and diagnose flaky or broken jobs so developers can fix CI failures without leaving their AI-powered tools.

MCP server for Jenkins CI/CD. Enables AI agents to trigger builds, inspect job and build status, stream console logs, and diagnose failing pipelines, bringing continuous integration workflows into AI-powered development environments.

Official LaunchDarkly MCP server for feature management. Lets AI agents create and toggle feature flags, manage targeting rules and segments, and inspect flag status across environments. Useful for progressive delivery, kill switches, experimentation, and automating flag lifecycle from an AI assistant.

Official Netlify MCP server for managing web deployments. Lets AI agents create and configure sites, trigger and monitor deploys, manage environment variables, and read build logs. Useful for shipping frontends and serverless functions, debugging failed builds, and automating deployment workflows from an AI assistant.

Official PagerDuty MCP server for incident management. Lets AI agents list and triage incidents, acknowledge and resolve them, look up on-call schedules, and query services so responders can manage operational incidents from AI-powered tools.

A powerful, native Go implementation of a Kubernetes MCP server with support for Kubernetes and OpenShift. Unlike kubectl wrappers, it interacts directly with the Kubernetes API server — no external CLI tools required. Distributed as a single lightweight binary for Linux, macOS, and Windows. Supports multi-cluster configurations, Helm chart management, Tekton pipelines, pod exec, log streaming, and optional OpenTelemetry distributed tracing.

Connects AI agents to Datadog for monitoring, observability, and incident management. Enables querying metrics, viewing traces, searching logs, and managing monitors programmatically. Supports dashboard creation, alert configuration, and SLO tracking through the Model Context Protocol.

Official Docker MCP server for container management. Enables AI agents to list, start, stop, and inspect Docker containers, manage images, view logs, and execute commands inside running containers. Supports Docker Compose operations for multi-container applications and provides container health monitoring capabilities.