"Scheduler that invokes the agent based on a predefined schedule" matching MCP servers:

Self-host a realtime voice-call companion for coding agents. Exposes an MCP endpoint that agents can poll as an alternate input stream.

Official MCP server for Theta EdgeCloud's On-Demand Model APIs, providing access to 20+ AI models including image generation, audio transcription, and LLMs, directly from MCP-compatible clients.

Enables AI agents to generate spoken audio locally on a GPU via the Chatterbox models, with zero-shot voice cloning from reference clips, multilingual output, and sentence-aligned chunking for long text.

Transcribes YouTube videos or audio files to Markdown, plain-text, and Word documents.

Enables voice assistants to find nearby service providers, connect users to live video representatives for natural conversation, and handle appointment booking. It also returns confirmed bookings so the assistant can add them to its calendar.

Serves a single set of typed, validated tool definitions to MCP clients over stdio, letting them perform real actions through one controlled executor. The same definitions power a speech-driven OpenAI tool-calling agent, so voice and MCP clients share identical, validated capabilities.

Enables small dairy and crop farmers in India to talk to a voice-friendly farm ledger, with every number verified rather than guessed. It is a self-hosted MCP server over Streamable HTTP, planned to run on AWS Lambda with DynamoDB and Amazon Bedrock.

Enables AI agents and applications to send audio notifications with text-to-speech, message streaming, agent-to-agent conversations, and web push notifications through a persistent message store and MCP integration.

Enables macOS users to hear short spoken summaries of agent replies using the built-in say command, while keeping full details in the chat. Works with any MCP-compatible host and requires no cloud or API key.

Enables AI coding assistants to verbally announce task progress and events through local text-to-speech, with cloud synthesis via Volcano Engine or Edge TTS, scene-based voice settings, and a Windows SAPI fallback so announcements continue even when the cloud fails.

Provides a catalog of paid micro-work tools for text processing, speech, and image generation with fixed USDC pricing via x402. Enables agents to discover capabilities, get quotes, and prepare calls without handling wallet keys.

Enables MCP-aware agents to fetch direct captions or download audio and transcribe with local Whisper from common video platforms like YouTube, Vimeo, and Twitch.

Provides local vision and audio perception for MCP-compatible agents, enabling them to read images, transcribe text from visual media, analyze videos, and convert speech to text entirely on-device. It is privacy-focused with no cloud upload or API keys by default, using Ollama for inference.

Local-only transcription server for MCP agents, powered by CrispASR. Transcribes audio/video files without cloud uploads, supporting English and Chinese.

Enables automated video learning workflows by ingesting video URLs, managing remote GPU ASR transcription, pulling transcripts, generating digest summaries, and searching local notes via MCP tools.

Enables hands-free voice collaboration with a coding agent: you speak requirements aloud and hear only short, plain-language progress and completion summaries while the agent edits code, runs tests, and fixes bugs in the background. It also handles continuous speech recognition with automatic turn detection, barge-in interruption, and spoken approval prompts before risky operations.

Profile-driven MCP server for Google Cloud Text-to-Speech that exposes tools to synthesize text to speech, run diagnostics, and stop playback, with voice and settings locked per profile.

Enables MCP clients to run project-scoped video editing workflows: propose and approve editing strategies, apply validated plans, review immutable versions, and export final renders via FFmpeg, with durable persistence and approval gates.

Enables AI assistants to download YouTube videos and transcribe them locally using OpenAI's Whisper speech recognition model.

A multi-agent human-computer interaction system that enables natural interaction through integrated visual recognition, speech recognition, and speech synthesis capabilities.