Developer Tools MCP 서버
developer tools를 위한 엄선된 MCP 서버 컬렉션을 찾아보세요. 5618개의 서버를 탐색하고 필요에 맞는 완벽한 MCP를 찾아보세요.
Bio-OS
Manages workflows and Docker image building for Bio-OS instance platforms.
Liveblocks
Enables AI agents to interact with the Liveblocks REST API for managing aspects like rooms, threads, comments, and more.
Kubernetes
Manages Kubernetes clusters using kubectl.
Aws
Enables natural language interaction with AWS resources through AI assistants like Claude.
DeepSeek
Provides code generation and completion capabilities using the DeepSeek API, with support for tool chaining and cost optimization.
Github Repo
Enables AI assistants to browse GitHub repositories, explore directories, and view file contents.
Jentic
Enables AI agents to rapidly discover and integrate external APIs and workflows without writing or maintaining API-specific code.
Pypi Query
Enables structured queries for Python packages and their associated GitHub repositories.
Shell Server
Executes shell commands via the Model Context Protocol (MCP) enabling safe execution of shell commands by AI agents.
X
Enables interaction with the X platform through an MCP client.
Cmd-Line Executor
Executes command-line commands and returns their output, status code, and errors.
Chroma
Integrates ChromaDB with Cursor IDE to create a persistent, searchable knowledge hub for AI-assisted development.
Agentipy
Enables Claude AI to interact with the Solana blockchain by providing on-chain tools through a standardized interface.
Mcp Docs
Provides Elixir project and dependency documentation to an LLM through an SSE MCP server.
EntraID
Provides a FastMCP server for interacting with Microsoft Graph API to manage EntraID (Azure AD) resources.
OrganiX Agent
Provides a personal AI agent platform with cross-platform capabilities and Model Context Protocol (MCP) integration, leveraging Claude 3.7.
iRacing
Integrates iRacing with the Model Context Protocol (MCP) to enable interaction with iRacing data.
Llm Bridge
Enables seamless integration with various Large Language Models (LLMs) through a model-agnostic Message Control Protocol (MCP) server.
Shared Knowledge
Enables AI assistants to share and utilize a common knowledge base for Retrieval Augmented Generation (RAG).
Telegram
Enables LLMs to send notifications to Telegram and receive user responses.
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