"Controlling Ableton Live music production software" matching MCP servers:

A production-grade Model Context Protocol server for PostgreSQL. Lets AI agents safely inspect, query, operate, and tune a Postgres database — over 100 tools spanning catalog introspection, query intelligence, natural-language SQL, structural diffs, hybrid search, graph queries, data movement, live ops, and more.

A production-grade MCP server and client implementation with comprehensive features including structured logging, health checks, metrics, authentication, and RAG capabilities with PostgreSQL vector search. Supports both stdio and SSE transports with containerization and security features for enterprise deployment.

A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.

Enables LLM hosts to retrieve live, relevant documentation excerpts from official library docs sites via a search-and-RAG tool, avoiding reliance on training data.

Provides AI agents with persistent, searchable memory using semantic search, auto-linking, and categorization, with zero-config local setup or production-ready external providers.

A production-grade semantic search server for food recipes — built for AI agents using the Model Context Protocol (MCP). Search across 50,000+ recipes with hybrid dense + sparse retrieval and cross-encoder reranking.

A production-grade MCP server for integrating RAG into AI agents, supporting multiple vector databases with enterprise security and dynamic tool selection.

A local vector database system that provides LLM coding agents with fast, efficient semantic search capabilities for software projects via the Message Control Protocol.

Enables Claude to index and retrieve context from codebases using self-hosted Milvus for semantic search, with hardened reliability and security for production use.

Exposes LangChain and Anthropic Claude capabilities as tools for generating production-ready RAG systems, Supabase vector stores, and document ingestion pipelines. It enables users to instantly scaffold AI infrastructure and document processing code through natural language prompts in MCP-compatible clients.

Semantic search server for code and documentation using Qdrant vector database. Supports multi-language indexing, live updates, and natural language queries.

Enables indexing and semantic search of software project files via MCP, with automatic file watching for updates.

Enables RAG-powered documentation search using OpenAI embeddings and Pinecone vector database. Provides an extensible framework for adding additional tools with support for both local STDIO and production HTTP transports.

An archived, unsafe MCP server with critical security vulnerabilities, classified as a pump and dump operation. Do not use in production.

Enables querying live Zora token data and analytics methodology through SQL and document retrieval tools, providing guarded access to on-chain holder, transfer, and token information.

A production-ready MCP server for persistent AI memory across LLMs like Claude and ChatGPT. Provides automatic conversation backup, multi-user support, and multi-storage (PostgreSQL, Redis, Qdrant).

Enables fast, on-device machine learning with production-grade persistence through the Model Context Protocol. It allows instant training of text classifiers using Extreme Learning Machines, with SurrealDB-backed persistence and monitoring.

Enables storing, querying, and managing decision traces with semantic search using Voyage AI embeddings and ChromaDB. Supports outcome tracking and category filtering for software development decisions.

Enables AI assistants to retrieve grounded, cited answers by first searching a local knowledge base and falling back to live web search when needed.