AI를 즐겨 사용하는 도구에 연결하기 위한 MCP 서버 전체 컬렉션을 살펴보세요.
Connects language models to a Qdrant vector database for storing and retrieving information.
Enables programmatic interaction with GitLab repositories and resources via a Model Context Protocol server.
Enables secure interaction with MySQL databases through a Model Context Protocol (MCP) server.
Enables collaboration between small, on-device LLMs and larger cloud-based models for cost-efficient language processing.
Facilitates the building, evaluation, and running of general multi-agent assistance systems.
Indexes and searches third-party library documentation using semantic splitting and vector embeddings.
Enables interaction with Text to Speech and audio processing APIs for MCP clients.
Autonomously evaluates web applications using LLM-powered agents within a code editor.
Enables developers to write Model Context Protocol servers and clients in Go with minimal code.
Provides example applications demonstrating the use of the Spring AI project.
Simplifies the development, running, evaluation, and orchestration of LLM-based agents using YAML and a CLI.
Converts existing RESTful and gRPC services into MCP-Server compatible services without infrastructure changes.
Facilitates the integration of AI models into daily workflows through agent-based frameworks and command-line tools.
Fetches and converts Deepwiki content to Markdown for use in code editors and other MCP-compatible clients.
Connects AI agents with Azure services, enabling exploration, querying, and management of Azure resources.
Enables Claude Code as a one-shot MCP server, allowing LLMs to directly leverage Claude's coding capabilities.
Connects MCP clients that only support local (stdio) servers to a remote MCP server, enabling authentication support.
Demonstrates security vulnerabilities in Model Context Protocol (MCP) implementations for educational purposes.
Enables advanced automation and interaction capabilities for Infrastructure as Code (IaC) development through integration with Terraform Registry APIs.
Compose data sources into a unified graph using this GraphQL Federation platform.
Provides a Java-based server and management platform for Xiaozhi ESP32 devices, enabling device monitoring, voice customization, role switching, and dialogue management.
Provides a robust and scalable operating system foundation for building, deploying, and managing intelligent AI agents.
Provides a Model Context Protocol (MCP) server for managing Kubernetes and OpenShift resources.
Transforms codebases into searchable knowledge bases for AI assistants, providing both semantic and regex search capabilities via the Model Context Protocol.
Enables AI agents to measure, exchange, and settle value on-chain within a decentralized network.
Automates the evaluation and debugging of web applications using a browser-powered agent directly within your code editor.
Autonomously evaluates web applications by deploying a browser-driving agent directly from your code editor.
Democratizes AI scientists by providing an ecosystem for creating powerful research partners from any large language model.
Empowers Large Language Models with a production-ready SwiftUI component library and full-stack recipes for building real iOS applications.
Optimizes AI agent interaction with codebases by enabling structured retrieval and significantly reducing token usage through precise AST parsing.
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