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Enables sending messages to any webhook endpoint, facilitating integration with MCP-compatible LLMs.
Coordinates agents by hosting finite state machines as dynamic resources, enabling real-time updates based on state changes.
Enables macOS native notifications, prompts, text-to-speech, screenshots, and file selection through MCP-compatible clients.
Connects to the Inkeep RAG service to retrieve product content and documentation.
Enables interaction with Jira instances, providing tools for querying, creating, editing, and deleting Jira tickets.
Augment AI responses with context by retrieving and processing documentation through vector search.
Analyzes the Solana memecoin ecosystem using AI and on-chain data.
Integrates Framelink Figma as a context server for Zed's Assistant.
Simplifies management of Model Context Protocol (MCP) servers and their integration across different clients.
Simplifies building Model Context Protocol (MCP) servers within NestJS applications using decorators.
Integrates ChromaDB with Cursor IDE to create a persistent, searchable knowledge hub for AI-assisted development.
Empower AI agents to remotely control graphical user interfaces on Windows, Linux, macOS, and other VNC-compatible systems.
Enables AI agents to query, discover, and analyze Prometheus metrics through natural language interactions via a unified interface.
Provides a Model Context Protocol server integrating Google AI Studio and Gemini API for multi-modal content generation, file processing, PDF-to-Markdown conversion, image analysis, and audio transcription.
Enables AI assistants to perform comprehensive CRUD operations and system management within EspoCRM via a Model Context Protocol (MCP) interface.
Empower LLMs to programmatically create, edit, and execute custom skills across MCP-compatible clients like Claude and Cursor.
Bridges AI assistants with the Arch Linux ecosystem, enabling intelligent and safe access to the Arch Wiki, AUR, and official repositories.
Enables Large Language Models to interact with the Wwise Authoring application by exposing custom WAAPI functions to Model Context Protocol clients.
Establishes a unified rules system for consistent coding standards across various AI assistants and development workflows.
Streamlines API test generation and execution using a YAML declarative approach, optimized for AI coding assistants.
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