Discover our curated collection of MCP servers for deployment & devops. Browse 1801servers and find the perfect MCPs for your needs.
Facilitates building and deploying fully-managed AI agents and long-running workflows with built-in durability and observability.
Orchestrates intelligent multi-agent swarms and autonomous workflows to build advanced AI systems.
Unifies metadata for data discovery, observability, and governance through a central repository, in-depth column-level lineage, and seamless team collaboration.
Provides a backend service for ESP32 devices, enabling rapid deployment of custom intelligent control servers.
Bring AWS best practices directly into development workflows with a suite of specialized Model Context Protocol (MCP) servers.
Provides an AI-native API gateway for cloud-native applications, extending Envoy and Istio with Wasm plugins.
Create and host AI companions with personalized personalities and conversational memory, accessible via browser or SMS.
Provides a self-hosted web interface and API for interacting with large language models via llama.cpp.
Provides containerized development environments, enabling multiple coding agents to work safely and independently with diverse tech stacks.
Provides an open-source, cloud-native LLMOps platform for designing, deploying, observing, and managing AI applications.
Manages Cloudflare resources using natural language commands via the Model Context Protocol (MCP).
Unifies Model Context Protocol (MCP) and REST services, providing a central management point for AI clients and federated environments.
Index and search code across multiple repositories and branches from various code hosting platforms.
Create and configure development containers from devcontainer.json files, providing isolated development environments.
Exposes MCP stdio-based servers over SSE or WebSockets, enabling remote access and integration.
Facilitates communication between MCP clients and servers by bridging different transport protocols like stdio and SSE.
Transforms existing API servers and services into Model Context Protocol (MCP) compliant endpoints with zero code changes.
Accelerate the development and research of complex Retrieval-Augmented Generation (RAG) systems with a low-code, modular framework.
Provides a Model Context Protocol (MCP) server for accessing and interacting with Grafana instances and their surrounding ecosystem.
Install other MCP servers via Claude by specifying their npm or PyPi package names or local paths.
Scroll for more results...