"Semantic layer for SQL query execution with dimensions and measures" matching MCP servers:

Local MCP server for private, consent-based speaker identification and attribution, with per-language voiceprints and calibrated match thresholds. It lets AI assistants know who is speaking in a room or transcript without cloud services.

Enables chat-driven audio analysis and enhancement using local Claude, including denoising, EQ, compression, and loudness normalization, with an A/B viewer for synchronized comparison.

Lets AI assistants query available voices from an OpenAI-compatible TTS service and synthesize text into audio with a chosen voice, tone, and speed, returning a public audio URL instead of Base64 data.

Enables MCP clients to leverage the Intel Arrow Lake NPU for local speech transcription, screenshot OCR, private semantic search, and hardware diagnostics, all processed locally.

MCP server that turns any audio or video source into structured, searchable intelligence for agents, enabling download, transcription, semantic search, speaker identification, and more.

MCP server for OpenHAB v5+ that exposes the entire REST API as tools for AI models, enabling complete control over items, things, rules, persistence, and semantic model.

Enables real-time voice agents and streaming audio pipelines to detect conversational turn completion, distinguishing finished speech turns from mid-sentence hesitation pauses using acoustic VAD and semantic scoring. It provides low-latency decision and telemetry output through a native MCP interface for integration with MCP-compliant clients.

Enables Cursor, Claude, and other MCP agents to interact with the HyperWhisper app for voice transcription, model management, and recording search.

Provides read-only access to local voice-to-text transcription history from the SayAll wireless mic app on macOS. Enables MCP-compatible assistants to query and paginate past transcripts by time and app with strong privacy boundaries.

Enables supported local AI clients to control a visible desktop presence through bounded presentation tools such as animations, window control, and short local speech over a loopback endpoint, without granting decision or execution authority.

Bi-directional communication for Claude Code via Voice Calls, SMS, and WhatsApp, allowing users to call Claude, receive callbacks, and interact through multiple channels.

Read-only MCP server for Telegram that enables reading messages and transcribing voice, audio, and video notes via the Telegram API for use with Codex and Claude Code.

Screen-aware voice command layer for AI agents that captures screen context and instructions to build agent-ready commands for various tools.

Enables ChatGPT on the web to generate and play spoken audio through an embedded player, exposing a speak tool that renders text with selectable styles and scenes via configurable text-to-speech providers. It routes each request across ElevenLabs, Cartesia, Azure Speech, and DashScope in a configured failover order, picking providers by language and scenario.

Windows-optimized MCP server for ElevenLabs text-to-speech and audio processing, with fixed file paths and Cursor IDE integration.

Enables MCP clients to place, monitor, and hang up phone calls through authenticated tools, retrieve transcripts, and exchange correlated task results with a waiting host. It supports simulated or live calling with persisted call lifecycle records, duration limits, and idempotent requests.

Enables Claude to interact with WhatsApp: read chats, search messages, send messages with a mandatory confirmation step, and transcribe voice notes locally, all with encrypted storage and prompt-injection scrubbing.

Enhanced MCP server for sending native macOS notifications with terminal-notifier, interactive dialogs, text-to-speech, screenshots, and file selection.

Enables AI agents to ring or text a user's actual iPhone, read a question or update aloud, and return the spoken answer back to the agent as text. Calls and messages land in a single titled thread per agent, with tools for polling results, waiting for replies, and labeling conversations.

Enables LLM agents to process local videos into timestamped, citable text documents and then query them through tools for listing videos, retrieving transcripts, and fetching specific segments, all fully offline.