"Information about DeepSeek and its Applications" matching MCP servers:

A local multimodal recognition workbench that uses your Edge/Chrome session to access DeepSeek Web, exposing MCP tools for recognizing images, documents, audio, and video with privacy-preserving temporary processing.

Local MCP server that adds multimodal capabilities to text-only models like Codex/DeepSeek, offering tools for image description, audio transcription, video analysis, image/video generation, and speech synthesis.

Enables agents to submit audio files to the local Meeting Transcriber macOS app, retrieve diarized transcripts, assign speaker names, and start or stop automatic meeting detection over its localhost automation API. Everything runs on-device, so no audio or transcripts leave the Mac.

Enables an AI assistant to have its recent code actions summarized aloud through system speakers, using offline native voices or cloud neural voices. It supports asynchronous playback and voice listing so users can hear what the agent did without reading the full response.

Analyze an hour-long YouTube video sentence by sentence in three minutes, and extract the information you need from the screen. Open-source local MCP server for AI clients.

Transcribe YouTube videos for LLM chat applications. Supports fetching transcripts in multiple languages and includes an ad-removal instruction.

Provides text-to-speech capabilities through the Model Context Protocol, allowing applications to easily integrate speech synthesis with customizable voices, adjustable speech speed, and cross-platform audio playback support.

Enables MCP clients to place real phone calls to businesses in the US and Canada, navigating phone menus, waiting on hold, asking the agent mid-call for information, and returning structured answers with transcripts. Supports booking, quotes, cancellations, and multi-business calls in parallel.

Enables LLM applications to make voice calls and send SMS messages through the Vonage API, allowing AI assistants to perform real-world telephony operations with support for speech recognition and customizable voice parameters.

Gives an AI assistant its own real phone number and email address so it can place and answer calls, book reservations by phone, send and receive texts, and read written summaries, structured details, and transcripts of every conversation. Also manages contacts, persona and voice settings, webhooks, and a unified call/message history through a hosted OAuth-secured MCP endpoint requiring no local code.

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.

Enables AI assistants to summarize, take notes on, and answer questions about YouTube, Bilibili, and Xiaohongshu videos by providing subtitles and local speech transcription with timestamps via MCP.

Enables AI agents to access local voice synthesis, zero-shot cloning, and voice catalog tools through native MCP tool calls for applications like Claude and Cursor.

Enables parsing Douyin video information, downloading videos without watermark, and transcribing speech to text using Tencent Cloud ASR.

Lets Claude convert its replies into natural spoken scripts and play them with pause, resume, and rewind controls, plus voice and engine selection.

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 an AI client to obtain the text of a YouTube video by returning its metadata, the captions YouTube already provides, or a Whisper transcription run on a local GPU. Text responses are bounded and pageable, and long transcriptions run as background jobs that survive request timeouts.

Provides voice recognition and text extraction capabilities with support for both stdio and MCP modes, processing audio files or base64 encoded data and returning structured results with language, emotion, and speaker information.

Enables end-to-end testing of voice-enabled web applications by simulating a synthetic user with a virtual microphone, virtual speakers, a real browser, and a viewable display, allowing audio injection, speech capture/transcription, and browser automation.

Feedback ingestion for AI agents. Record your screen and talk; your agent does the rest — files the bugs, writes the spec, builds the backlog.