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Provides access to large language and vision-language models based on linear attention for various natural language and multimodal tasks.
Manages Cloudflare resources using natural language commands via the Model Context Protocol (MCP).
Provides a quick start guide for programming with the Model Context Protocol (MCP) in Chinese.
Connect AI models to thousands of APIs, data sources, and tools with a single function call, abstracting away the complexities of the Model Context Protocol.
Enables developers to integrate Google's generative models into Python applications.
Provides an open-source, cloud-native LLMOps platform for designing, deploying, observing, and managing AI applications.
Experience AI Xiaozhi's voice and smart assistant functionalities through a versatile Python-based client, enabling access without dedicated hardware.
Enables Java applications to interact with AI models and tools through a standardized interface.
Enables LLMs to control cloud browsers for web interaction, data extraction, and task automation using Browserbase and Stagehand.
Serve local large language models natively on Apple Silicon with OpenAI/Ollama-compatible APIs, supporting tool calling, a plugin ecosystem, and a menu bar chat UI.
Index and search code across multiple repositories and branches from various code hosting platforms.
Provides a TypeScript framework for constructing Model Context Protocol (MCP) servers.
Programmatically assembles prompts for LLMs using JavaScript to orchestrate LLMs, tools, and data in code.
Documents potential implementations for Nostr-compatible relay and client software.
Build Java enterprise applications with a restrained, efficient, open, and ecological framework.
Enables communication between Unity and Large Language Models for automating workflows and controlling the Unity Editor.
Provides all-in-one infrastructure for search, recommendations, Retrieval-Augmented Generation (RAG), and analytics via API.
Provides AI assistants with comprehensive access to shadcn/ui v4 components, blocks, demos, and metadata via the Model Context Protocol.
Accelerate the development and research of complex Retrieval-Augmented Generation (RAG) systems with a low-code, modular framework.
Provides a catalog of official Microsoft MCP server implementations for AI-powered data access and tool integration.
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