Ffvoice

Offline speech-to-text & speaker diarization MCP server: transcribe audio on-device, no cloud

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "ffvoice": {
      "command": "npx",
      "args": [
        "-y",
        "ffvoice"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

ffvoice-engine

🎙️ Offline speech-to-text & speaker diarization for AI agents — Whisper ASR, live captioning, an MCP server, a CLI and Python bindings. Fully on-device, no cloud API.

🎙️ 离线语音识别 + 说话人分离,AI Agent 开箱即用 —— Whisper 实时转写 · 实时字幕 · MCP server · CLI · Python 绑定 · 100% 本地运行,音频不上云。

ffvoice demo — offline speech-to-text in ~12 lines of Python

Why ffvoice? / 为什么用 ffvoice？

The honest pitch: ffvoice is an integration layer, not a new ASR engine. It embeds whisper.cpp as-is and makes no changes to its accuracy or inference speed. What ffvoice adds is a batteries-included, pre-wired pipeline — microphone capture → RNNoise denoising → VAD segmentation → Whisper ASR → speaker diarization → live captions / WAV / FLAC / subtitles — delivered as a single C++ SDK with Python bindings, a CLI, and an MCP server that lets AI agents (Claude and others) transcribe audio out of the box — all in one pip install or cmake build.

诚实定位: ffvoice 是一个集成层，而非新的 ASR 引擎。它内嵌 whisper.cpp，不修改其识别精度或推理速度。ffvoice 带来的是一条开箱即用、预连接的完整管道——麦克风采集 → RNNoise 降噪 → VAD 分段 → Whisper ASR → 说话人分离 → 实时字幕 / WAV / FLAC / 字幕输出——打包成 C++ SDK + Python 绑定 + CLI，外加一个 MCP server,让 AI agent(Claude 等)开箱即用地转写音频——一条 pip install 或 cmake 即可完成。

Pain points it addresses / 解决的痛点

Pain point	ffvoice approach
Privacy / 隐私合规 — audio must not leave the device (GDPR, HIPAA, enterprise policy)	100% offline; audio never transmitted
Cloud cost / 云端费用 — commercial APIs charge per minute ($0.01–0.024/min at scale)	Zero per-minute cost; runs on your own hardware
Glue code / 胶水代码 — wiring PortAudio + RNNoise + VAD + whisper.cpp + FLAC yourself takes days	All wired together and tested; one SDK
Offline / 断网场景 — embedded systems, air-gapped environments, poor connectivity	Fully offline; no network dependency
Low latency / 低延迟 — cloud round-trips add 200–800ms per request	Local inference; < 100ms capture latency

What ffvoice does NOT do / 不做什么

ffvoice does not improve whisper.cpp's WER (word error rate) or speed. If Whisper tiny gives you 12% WER, ffvoice will too.
ffvoice does not outperform sherpa-onnx or other optimized inference runtimes on raw transcription speed.
ffvoice does not provide custom vocabulary or acoustic model fine-tuning.

If raw ASR accuracy or throughput is your primary concern, evaluate whisper.cpp directly or consider specialized runtimes. ffvoice's value is the integrated pipeline, not the ASR engine itself.

📋 项目介绍

ffvoice-engine 是一个轻量级、高性能的音频处理引擎，专注于实时音频采集、智能处理和语音识别。

🎯 使用场景

📝 会议记录 - 实时转写会议内容，说话人分离标注"谁说了什么"，自动生成字幕
🎓 在线教育 - 录制课程并生成字幕，支持多语言识别
🎙️ 播客制作 - 高质量音频录制 + RNNoise 降噪 + 自动字幕生成
🎵 音乐制作 - 低延迟音频采集，支持 FLAC 无损压缩
🤖 语音助手 - 实时语音识别和处理，构建本地 AI 语音应用
📡 直播字幕 - 边录边转写，生成实时字幕流

✨ 核心优势

vs 商业服务（Azure/Google Cloud Speech）:

✅ 完全离线 - 无需网络，保护隐私，零 API 费用
✅ 低延迟 - 本地处理，<100ms 音频采集延迟
✅ 开源免费 - MIT 协议，可商用

vs FFmpeg 命令行:

✅ 实时转写 - 边录边识别，支持 VAD 智能分段
✅ AI 降噪 - 集成 RNNoise 深度学习降噪
✅ C++ SDK - 可嵌入任何 C++ 应用，非黑盒工具

vs Python 方案（whisper-cli）:

✅ 原生管道 - C++20 实现，处理链无 Python 解释器与胶水开销
✅ 易部署 - 单一可执行文件，无 Python 环境依赖
✅ 同样的精度 - 内嵌 whisper.cpp，识别质量与上游一致（ffvoice 不改模型）

💡 技术亮点

🚀 零拷贝处理链 - 音频数据在内存中就地处理
🧠 智能 VAD 分段 - 基于 RNNoise VAD 的语音活动检测
🎯 高压缩比 - FLAC 无损压缩 2-3x，质量无损
⚡ whisper.cpp 推理 - 内嵌 whisper.cpp，Apple Silicon 上推理快于实时

核心特性

✅ 实时音频采集 - 低延迟麦克风/系统声音捕获 (PortAudio)
✅ 多格式输出 - WAV、FLAC 无损压缩
✅ 音频增强处理 - 音量归一化、高通滤波、RNNoise 降噪
✅ 离线语音识别 - Whisper ASR (tiny model，纯文本/SRT/VTT/JSON 四种格式，含词级时间戳)
✅ 实时字幕流 - LiveCaptioner 双线程模型，partial/final 字幕事件，边说边出字
✅ 说话人分离 - Diarizer 离线 diarization（sherpa-onnx），自动标注"谁在何时说话"
✅ Agent 集成 - CLI + MCP server，AI agent 可直接调用本地离线语音能力

🏗️ 当前状态 (v0.8.3)

四个集成层路线图阶段全部交付，已发布到 PyPI（macOS / Linux / Windows，Python 3.10–3.14）。

能力	状态
音频采集 / WAV·FLAC 输出 / 音频增强（归一化·高通·RNNoise）	✅
离线语音识别（Whisper ASR — 纯文本 / SRT / VTT / JSON，词级时间戳）	✅
实时字幕流（LiveCaptioner — partial/final 字幕事件）	✅
说话人分离（Diarizer — sherpa-onnx，可选 `-DENABLE_DIARIZATION=ON`）	✅
Agent 集成（CLI 硬化 + MCP server，5 个工具）	✅
测试：311 C++ 单元测试 + 131 Python 测试，全部通过	✅

完整历史见 CHANGELOG.md。

🚀 快速开始

依赖

CMake 3.20+
C++20 编译器（GCC 10+, Clang 12+, MSVC 2019+）
FFmpeg 4.4+ (libavcodec, libavformat, libavutil, libswresample)
PortAudio 19.7+ (音频采集)
FLAC 1.5+ (无损压缩)
whisper.cpp (可选，自动下载，用于语音识别)
RNNoise (可选，自动下载，用于深度学习降噪)

macOS 安装：

bash

brew install cmake ffmpeg portaudio flac

Linux (Ubuntu/Debian) 安装：

bash

sudo apt-get install cmake build-essential \
  libavcodec-dev libavformat-dev libavutil-dev libswresample-dev \
  portaudio19-dev libflac-dev

Windows 安装：

powershell

# 使用 vcpkg 管理 C++ 依赖
# 1. 克隆 vcpkg（如果还没有）
git clone https://github.com/Microsoft/vcpkg.git C:\vcpkg
C:\vcpkg\bootstrap-vcpkg.bat

# 2. 安装依赖包
C:\vcpkg\vcpkg install ffmpeg:x64-windows portaudio:x64-windows libflac:x64-windows

# 3. 设置环境变量（用于 CMake）
set CMAKE_TOOLCHAIN_FILE=C:\vcpkg\scripts\buildsystems\vcpkg.cmake

# 注意：Windows 用户也可以直接使用 PyPI 的预编译 wheels（推荐）
# pip install ffvoice

编译

标准编译:

Linux/macOS:

bash

mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j$(nproc)

Windows:

powershell

mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=C:\vcpkg\scripts\buildsystems\vcpkg.cmake
cmake --build . --config Release

启用 RNNoise 降噪 (推荐，自动下载):

Linux/macOS:

bash

mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DENABLE_RNNOISE=ON
make -j$(nproc)
# RNNoise 库会通过 CMake FetchContent 自动下载和编译

Windows:

powershell

# 注意：Windows 版本禁用 RNNoise（MSVC 不支持 VLA）
# 使用其他音频处理选项替代

启用 Whisper 语音识别 (推荐，自动下载):

Linux/macOS:

bash

mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DENABLE_WHISPER=ON
make -j$(nproc)
# whisper.cpp 和 tiny 模型（39MB）会自动下载

Windows:

powershell

mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DENABLE_WHISPER=ON -DCMAKE_TOOLCHAIN_FILE=C:\vcpkg\scripts\buildsystems\vcpkg.cmake
cmake --build . --config Release

启用所有可选功能 (Linux/macOS):

bash

cmake .. -DCMAKE_BUILD_TYPE=Release \
  -DENABLE_RNNOISE=ON \
  -DENABLE_WHISPER=ON
make -j$(nproc)

使用

注意:

Linux/macOS: 使用 ./build/ffvoice

Windows: 使用 .\build\Release\ffvoice.exe

bash

# 查看帮助
./build/ffvoice --help

# 生成测试 WAV 文件（440Hz A4 音符，3秒）
./build/ffvoice --test-wav test.wav

# 列出可用音频设备
./build/ffvoice --list-devices

# 录制 10 秒 WAV 音频（默认格式）
./build/ffvoice --record -o recording.wav -t 10

# 录制 30 秒 FLAC 音频（无损压缩）
./build/ffvoice --record -o recording.flac -t 30

# 使用最大压缩级别录制 FLAC
./build/ffvoice --record -o recording.flac --compression 8 -t 60

# 选择特定设备录制立体声
./build/ffvoice --record -d 1 -o stereo.wav --channels 2 -t 20

# 启用音频处理（音量归一化 + 高通滤波）
./build/ffvoice --record -o clean.wav --enable-processing -t 10

# 仅启用音量归一化
./build/ffvoice --record -o normalized.wav --normalize -t 10

# 自定义高通滤波频率（去除 100Hz 以下噪声）
./build/ffvoice --record -o filtered.flac --highpass 100 -t 20

Read the full README →View source on GitHub →

Related MCP Servers

View all in Speech-to-Text View all alternatives

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Ffvoice

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "ffvoice": { "command": "npx", "args": ["-y", "ffvoice"] } }

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

Live Dynamic SVG Preview

Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/ffvoice?style=directory)](https://allmcps.com/mcp/ffvoice)

HTML Embed

<a href="https://allmcps.com/mcp/ffvoice"><img src="https://allmcps.com/api/badge/ffvoice?style=directory" alt="Ffvoice on AllMCPs" /></a>

Technical Specs & Signals

CategorySpeech-to-Text

27Quality signal: Emerging · 27/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20

Documentation & tools11/30

Adoption & activity1/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

★ Spotlight Slot

Feature Your MCP Server

Get maximum visibility for your server across our directory, search results, and detail pages.

Spotlight Your Server

Own this project?

This directory is pre-filled from public sources. Claim via GitHub README, site badge, or DNS TXT to unlock edit access and the Official badge and attach your website — proof is checked automatically, then reviewed by our team.

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Explore more

More in 🎙️ Speech-to-Text →Alternatives to Ffvoice →Install in Claude Desktop Install in Cursor Install in VS Code

Ffvoice

Offline speech-to-text & speaker diarization MCP server: transcribe audio on-device, no cloud

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "ffvoice": {
      "command": "npx",
      "args": [
        "-y",
        "ffvoice"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

ffvoice-engine

🎙️ Offline speech-to-text & speaker diarization for AI agents — Whisper ASR, live captioning, an MCP server, a CLI and Python bindings. Fully on-device, no cloud API.

🎙️ 离线语音识别 + 说话人分离,AI Agent 开箱即用 —— Whisper 实时转写 · 实时字幕 · MCP server · CLI · Python 绑定 · 100% 本地运行,音频不上云。

ffvoice demo — offline speech-to-text in ~12 lines of Python

Why ffvoice? / 为什么用 ffvoice？

Pain points it addresses / 解决的痛点

Pain point	ffvoice approach
Privacy / 隐私合规 — audio must not leave the device (GDPR, HIPAA, enterprise policy)	100% offline; audio never transmitted
Cloud cost / 云端费用 — commercial APIs charge per minute ($0.01–0.024/min at scale)	Zero per-minute cost; runs on your own hardware
Glue code / 胶水代码 — wiring PortAudio + RNNoise + VAD + whisper.cpp + FLAC yourself takes days	All wired together and tested; one SDK
Offline / 断网场景 — embedded systems, air-gapped environments, poor connectivity	Fully offline; no network dependency
Low latency / 低延迟 — cloud round-trips add 200–800ms per request	Local inference; < 100ms capture latency

What ffvoice does NOT do / 不做什么

ffvoice does not improve whisper.cpp's WER (word error rate) or speed. If Whisper tiny gives you 12% WER, ffvoice will too.
ffvoice does not outperform sherpa-onnx or other optimized inference runtimes on raw transcription speed.
ffvoice does not provide custom vocabulary or acoustic model fine-tuning.

If raw ASR accuracy or throughput is your primary concern, evaluate whisper.cpp directly or consider specialized runtimes. ffvoice's value is the integrated pipeline, not the ASR engine itself.

📋 项目介绍

ffvoice-engine 是一个轻量级、高性能的音频处理引擎，专注于实时音频采集、智能处理和语音识别。

🎯 使用场景

📝 会议记录 - 实时转写会议内容，说话人分离标注"谁说了什么"，自动生成字幕
🎓 在线教育 - 录制课程并生成字幕，支持多语言识别
🎙️ 播客制作 - 高质量音频录制 + RNNoise 降噪 + 自动字幕生成
🎵 音乐制作 - 低延迟音频采集，支持 FLAC 无损压缩
🤖 语音助手 - 实时语音识别和处理，构建本地 AI 语音应用
📡 直播字幕 - 边录边转写，生成实时字幕流

✨ 核心优势

vs 商业服务（Azure/Google Cloud Speech）:

✅ 完全离线 - 无需网络，保护隐私，零 API 费用
✅ 低延迟 - 本地处理，<100ms 音频采集延迟
✅ 开源免费 - MIT 协议，可商用

vs FFmpeg 命令行:

✅ 实时转写 - 边录边识别，支持 VAD 智能分段
✅ AI 降噪 - 集成 RNNoise 深度学习降噪
✅ C++ SDK - 可嵌入任何 C++ 应用，非黑盒工具

vs Python 方案（whisper-cli）:

✅ 原生管道 - C++20 实现，处理链无 Python 解释器与胶水开销
✅ 易部署 - 单一可执行文件，无 Python 环境依赖
✅ 同样的精度 - 内嵌 whisper.cpp，识别质量与上游一致（ffvoice 不改模型）

💡 技术亮点

🚀 零拷贝处理链 - 音频数据在内存中就地处理
🧠 智能 VAD 分段 - 基于 RNNoise VAD 的语音活动检测
🎯 高压缩比 - FLAC 无损压缩 2-3x，质量无损
⚡ whisper.cpp 推理 - 内嵌 whisper.cpp，Apple Silicon 上推理快于实时

核心特性

✅ 实时音频采集 - 低延迟麦克风/系统声音捕获 (PortAudio)
✅ 多格式输出 - WAV、FLAC 无损压缩
✅ 音频增强处理 - 音量归一化、高通滤波、RNNoise 降噪
✅ 离线语音识别 - Whisper ASR (tiny model，纯文本/SRT/VTT/JSON 四种格式，含词级时间戳)
✅ 实时字幕流 - LiveCaptioner 双线程模型，partial/final 字幕事件，边说边出字
✅ 说话人分离 - Diarizer 离线 diarization（sherpa-onnx），自动标注"谁在何时说话"
✅ Agent 集成 - CLI + MCP server，AI agent 可直接调用本地离线语音能力

🏗️ 当前状态 (v0.8.3)

四个集成层路线图阶段全部交付，已发布到 PyPI（macOS / Linux / Windows，Python 3.10–3.14）。

能力	状态
音频采集 / WAV·FLAC 输出 / 音频增强（归一化·高通·RNNoise）	✅
离线语音识别（Whisper ASR — 纯文本 / SRT / VTT / JSON，词级时间戳）	✅
实时字幕流（LiveCaptioner — partial/final 字幕事件）	✅
说话人分离（Diarizer — sherpa-onnx，可选 `-DENABLE_DIARIZATION=ON`）	✅
Agent 集成（CLI 硬化 + MCP server，5 个工具）	✅
测试：311 C++ 单元测试 + 131 Python 测试，全部通过	✅

完整历史见 CHANGELOG.md。

🚀 快速开始

依赖

CMake 3.20+
C++20 编译器（GCC 10+, Clang 12+, MSVC 2019+）
FFmpeg 4.4+ (libavcodec, libavformat, libavutil, libswresample)
PortAudio 19.7+ (音频采集)
FLAC 1.5+ (无损压缩)
whisper.cpp (可选，自动下载，用于语音识别)
RNNoise (可选，自动下载，用于深度学习降噪)

macOS 安装：

bash

brew install cmake ffmpeg portaudio flac

Linux (Ubuntu/Debian) 安装：

bash

sudo apt-get install cmake build-essential \
  libavcodec-dev libavformat-dev libavutil-dev libswresample-dev \
  portaudio19-dev libflac-dev

Windows 安装：

powershell

# 使用 vcpkg 管理 C++ 依赖
# 1. 克隆 vcpkg（如果还没有）
git clone https://github.com/Microsoft/vcpkg.git C:\vcpkg
C:\vcpkg\bootstrap-vcpkg.bat

# 2. 安装依赖包
C:\vcpkg\vcpkg install ffmpeg:x64-windows portaudio:x64-windows libflac:x64-windows

# 3. 设置环境变量（用于 CMake）
set CMAKE_TOOLCHAIN_FILE=C:\vcpkg\scripts\buildsystems\vcpkg.cmake

# 注意：Windows 用户也可以直接使用 PyPI 的预编译 wheels（推荐）
# pip install ffvoice

编译

标准编译:

Linux/macOS:

bash

mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j$(nproc)

Windows:

powershell

mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=C:\vcpkg\scripts\buildsystems\vcpkg.cmake
cmake --build . --config Release

启用 RNNoise 降噪 (推荐，自动下载):

Linux/macOS:

bash

mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DENABLE_RNNOISE=ON
make -j$(nproc)
# RNNoise 库会通过 CMake FetchContent 自动下载和编译

Windows:

powershell

# 注意：Windows 版本禁用 RNNoise（MSVC 不支持 VLA）
# 使用其他音频处理选项替代

启用 Whisper 语音识别 (推荐，自动下载):

Linux/macOS:

bash

mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DENABLE_WHISPER=ON
make -j$(nproc)
# whisper.cpp 和 tiny 模型（39MB）会自动下载

Windows:

powershell

mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DENABLE_WHISPER=ON -DCMAKE_TOOLCHAIN_FILE=C:\vcpkg\scripts\buildsystems\vcpkg.cmake
cmake --build . --config Release

启用所有可选功能 (Linux/macOS):

bash

cmake .. -DCMAKE_BUILD_TYPE=Release \
  -DENABLE_RNNOISE=ON \
  -DENABLE_WHISPER=ON
make -j$(nproc)

使用

注意:

Linux/macOS: 使用 ./build/ffvoice

Windows: 使用 .\build\Release\ffvoice.exe

bash

# 查看帮助
./build/ffvoice --help

# 生成测试 WAV 文件（440Hz A4 音符，3秒）
./build/ffvoice --test-wav test.wav

# 列出可用音频设备
./build/ffvoice --list-devices

# 录制 10 秒 WAV 音频（默认格式）
./build/ffvoice --record -o recording.wav -t 10

# 录制 30 秒 FLAC 音频（无损压缩）
./build/ffvoice --record -o recording.flac -t 30

# 使用最大压缩级别录制 FLAC
./build/ffvoice --record -o recording.flac --compression 8 -t 60

# 选择特定设备录制立体声
./build/ffvoice --record -d 1 -o stereo.wav --channels 2 -t 20

# 启用音频处理（音量归一化 + 高通滤波）
./build/ffvoice --record -o clean.wav --enable-processing -t 10

# 仅启用音量归一化
./build/ffvoice --record -o normalized.wav --normalize -t 10

# 自定义高通滤波频率（去除 100Hz 以下噪声）
./build/ffvoice --record -o filtered.flac --highpass 100 -t 20

Read the full README →View source on GitHub →

Related MCP Servers

View all in Speech-to-Text View all alternatives

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Ffvoice

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "ffvoice": { "command": "npx", "args": ["-y", "ffvoice"] } }

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

Live Dynamic SVG Preview

Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/ffvoice?style=directory)](https://allmcps.com/mcp/ffvoice)

HTML Embed

<a href="https://allmcps.com/mcp/ffvoice"><img src="https://allmcps.com/api/badge/ffvoice?style=directory" alt="Ffvoice on AllMCPs" /></a>

Technical Specs & Signals

CategorySpeech-to-Text

27Quality signal: Emerging · 27/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20

Documentation & tools11/30

Adoption & activity1/15

Community engagement0/10

★ Spotlight Slot

Feature Your MCP Server

Get maximum visibility for your server across our directory, search results, and detail pages.

Spotlight Your Server

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Explore more

More in 🎙️ Speech-to-Text →Alternatives to Ffvoice →Install in Claude Desktop Install in Cursor Install in VS Code