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Enhances code development by providing intelligent suggestions, documentation integration, and technology detection.
Provides file backup and restoration capabilities for AI agents and code editing tools.
Captures browser console logs and provides them to Cursor IDE through the Model Context Protocol.
Pins third-party dependencies to immutable digests using the Model Context Protocol.
Automates workflows and controls applications through natural language instructions.
Retrieves SonarQube project quality metrics programmatically via the FastMCP framework.
Enables robotic control via a Model Context Protocol (MCP) server, facilitating automation tasks.
Enables sending MIDI sequences to any software supporting MIDI input.
Converts OpenAPI specifications into MCP tools.
Provides a standardized interface for interacting with Scikit-learn models and datasets using the Model Context Protocol.
Integrates AI tools and agents with Cashfree APIs (Payment Gateway, Payouts, and SecureID) using the Model Context Protocol (MCP).
Empowers AI assistants with real-world capabilities through a standardized protocol for interacting with services like Gmail, Calendar, Tasks, Git, and note management.
Enables users to explore data and prototype projections within KurrentDB.
Cryptographically signs and verifies AI agent tool schemas to prevent supply-chain attacks.
Enables AI assistants to directly manage translations and projects within Weblate via the Model Context Protocol.
Connects Signoz instances with AI assistants and other MCP-compatible clients to enable programmatic data access and analysis.
Empower AI coding assistants to generate pixel art characters, animations, and tilesets directly within coding environments.
Empowers AI models to perform comprehensive MySQL database operations through a standardized Model Context Protocol interface.
Facilitates executing SQL queries on PostgreSQL databases with configurable read-only or write access, providing structured results for MCP-compatible tools.
Provides a sophisticated Retrieval-Augmented Generation (RAG) system for ingesting, indexing, and semantically searching Vaadin documentation.
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