发现我们为 deployment & devops 精心策划的 MCP 服务器集合。浏览 1974 个服务器,找到满足您需求的完美 MCP。
Enables Large Language Models to execute AWS Lambda functions as tools via Anthropic's Model Control Protocol (MCP) without code modifications.
Enables interaction with JFrog Platform APIs for repository management, build tracking, and more.
Integrates Zabbix monitoring capabilities with AI assistants and MCP-compatible tools, enabling comprehensive control over hosts, items, triggers, and problems.
Integrate with the Datadog API to manage incidents, monitors, logs, dashboards, metrics, traces, and hosts.
Orchestrates multiple MCP tools on-demand, drastically reducing token usage for AI agent contexts.
Generates AI-optimized context for code analysis and enforces extreme quality standards to make agentic code more deterministic.
Enables AI agents to connect with Apache Spark History Servers for intelligent job analysis and performance monitoring.
Enables efficient handling of filesystem operations through a fast and asynchronous MCP server.
Provides a flexible server and web application for deploying Hugging Face Hub API and search endpoints.
Enables AI assistants to interact with and manage Azure Kubernetes Service (AKS) clusters.
Enables interaction with NetBox data via Large Language Models (LLMs) using the Model Context Protocol (MCP).
Execute C# code online securely with container isolation and high-performance features.
Dynamically creates and manages Model Context Protocol (MCP) servers as child processes, enabling a flexible MCP ecosystem.
Scans code repositories locally for secrets, Infrastructure as Code misconfigurations, software composition analysis vulnerabilities, and static application security testing issues.
Inspects and analyzes running Go processes to provide insights into goroutine states and memory usage.
Enables seamless integration of VictoriaMetrics with Model Context Protocol (MCP) clients for enhanced monitoring, observability, and debugging.
Enables secure terminal command execution, directory navigation, and file system operations through a standardized interface.
Connects AI assistants to Octopus Deploy instances via the Model Context Protocol (MCP), enabling them to interact with your software delivery systems.
Manages Railway account resources and automates workflows through a local Model Context Protocol server.
Exposes Portainer environment data through the Model Context Protocol (MCP), enabling AI assistants to interact with containerized infrastructure.
Scroll for more results...