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Enables AI assistants to search and analyze arXiv papers through a simple Model Context Protocol interface.
Enables AI assistants to interact with the Godot game engine by launching the editor, running projects, and capturing debug output.
Converts various file types and web content into Markdown format.
Enables communication between Unity and Large Language Models for automating workflows and controlling the Unity Editor.
Provides a Model Context Protocol (MCP) server for accessing and interacting with Grafana instances and their surrounding ecosystem.
Programmatically assembles prompts for LLMs using JavaScript to orchestrate LLMs, tools, and data in code.
Connects Supabase projects to AI assistants via the Model Context Protocol (MCP).
Documents potential implementations for Nostr-compatible relay and client software.
Provides all-in-one infrastructure for search, recommendations, Retrieval-Augmented Generation (RAG), and analytics via API.
Exposes MCP stdio-based servers over SSE or WebSockets, enabling remote access and integration.
Provides a TypeScript framework for constructing Model Context Protocol (MCP) servers.
Build Java enterprise applications with a restrained, efficient, open, and ecological framework.
Provides a catalog of official Microsoft MCP server implementations for AI-powered data access and tool integration.
Create and configure development containers from devcontainer.json files, providing isolated development environments.
Captures screenshots on macOS without user interaction, enabling AI assistants to visually analyze UI bugs, designs, and data.
Serve, benchmark, and deploy large language models (LLMs) on various hardware platforms.
Provides AI assistants with comprehensive access to shadcn/ui v4 components, blocks, demos, and metadata via the Model Context Protocol.
Provides a high-performance, resilient AI gateway for connecting to multiple large language model providers through a single, unified API.
Enables AI agents to control web browsers via a Chrome extension, offering full Playwright API access with minimal context overhead.
Automates machine learning research workflows, including idea generation, experiment execution, and iterative paper review and refinement.
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