原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

deAPI MCP Server

作者:deapi-ai版本 0.6.2i★ –i在云端运行
注册名称io.github.deapi-ai/mcp-server-deapi

安装

概览

作者提供的说明(英文原文)

39 AI tools: transcription, video description, image & video generation, TTS, music, OCR via deAPI

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在云端运行
  • 传输方式: Streamable HTTP
  • 分类: 设计、图片与音频

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

你需要

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

适用于网页版、桌面版和手机版 Claude:

  1. 打开 Settings → Connectors
  2. 点击 Add custom connector
  3. 粘贴下面的地址,然后点击 Add
  4. 如果要求登录,请登录作者的服务
SettingsConnectors1+ Add2https://…3
https://mcp.deapi.ai/mcp

在终端中运行这条命令

claude mcp add --transport http deapi https://mcp.deapi.ai/mcp

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "deapi": {
      "url": "https://mcp.deapi.ai/mcp"
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "deapi": {
      "type": "http",
      "url": "https://mcp.deapi.ai/mcp"
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

ChatGPT 只能连接互联网上的服务器:

  1. 打开网页版 ChatGPT → Settings → Connectors (Apps)
  2. 点击 Create 或 Add(部分套餐需要先在 Advanced 中开启 Developer mode)
  3. 粘贴下面的地址,起个名字,然后保存
  4. 如果要求登录,请登录作者的服务
SettingsConnectors1+ Create2https://…3
https://mcp.deapi.ai/mcp

菜单名称可能因套餐和版本不同而略有差异。

你的选择通过 Cookie 保存在这台设备上,无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

服务器要求先登录,才会列出它的工具。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 deapi-ai 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • ✓ 通过

    端点有响应

    在 816 毫秒内响应(HTTP 401)

  • ✓ 通过

    使用 OAuth 登录

    声明支持 OAuth 登录,你的 AI 应用可以带你去登录

  • … 尚未检查

    工具描述中没有隐藏指令

    尚未检查;我们会在检查端点时读取工具列表

与“设计、图片与音频”中的其他服务器比较i

  • 星标数多于此分类中其他 2165 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 2165 个服务器的 97% 更新
  • 基础检查结果等于或优于此分类中其他 2165 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

deAPI MCP Server 安全吗?

它在 3 项适用的基础检查中通过了 3 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端?

它在作者的云端运行。通过互联网连接,无需安装,但你发送的内容会经过作者的服务器。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.6.2)于昨天进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接,所以接受远程 MCP 服务器的应用也可以使用,例如开启开发者模式的 ChatGPT。

有什么可以替代它?

同一分类中有:Figma Context MCP, Claude Real Video, Figma MCP Server

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Figma Context MCP

作者:GLips

Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.

在你的电脑上运行★ 16,165MIT5个月前更新
安装

Claude Real Video

作者:HUANGCHIHHUNGLeo

Let any LLM watch a video locally — and search everything it has ever watched.

在你的电脑上运行★ 2,284MIT2个月前更新
安装

Figma MCP Server

作者:figma

The Figma MCP server brings Figma design context directly into your AI workflow.

在云端运行★ 2,15213个月前更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

deAPI MCP Server

Production-ready Model Context Protocol (MCP) server for the deAPI REST API. This server exposes all deAPI AI capabilities as MCP tools, enabling LLMs to perform audio transcription, image generation, OCR, video generation, text-to-speech, and more.

deAPI v2 — this server targets the deAPI v2 client API (OpenAI-aligned noun paths under /api/v2/*). API v1 is not supported: the tools send v2 payloads only.

Features

  • Complete API Coverage: 39 deAPI tools exposed via MCP, covering every v2 client endpoint
  • Smart Adaptive Polling: Automatically polls async jobs with optimized intervals based on job type
  • OAuth 2.0 Authentication: Secure token exchange via OAuth Authorization Code flow with PKCE
  • Error Recovery: Automatic retry logic with exponential backoff
  • Progress Reporting: Real-time progress updates to MCP clients
  • Type Safety: Full Pydantic schema validation
  • Production Ready: Built with FastMCP framework for reliability

Available Tools

Audio Tools
  • audio_transcription - Transcribe audio files to text using Whisper models
  • audio_transcription_price - Calculate transcription cost
  • text_to_audio - Convert text to natural speech (TTS)
  • text_to_audio_price - Calculate TTS cost
  • text_to_music - Generate music from text description and lyrics
  • text_to_music_price - Calculate music generation cost
  • audio_url_transcription - Transcribe audio from URLs of completed Twitter Spaces
  • audio_url_transcription_price - Calculate Twitter Spaces transcription cost
Video Transcription Tools
  • video_file_transcription - Transcribe video files to text
  • video_file_transcription_price - Calculate video file transcription cost
  • video_url_transcription - Transcribe videos from URLs (YouTube, Twitter/X, Twitch, Kick, TikTok)
  • video_url_transcription_price - Calculate video URL transcription cost
Image Tools
  • text_to_image - Generate images from text prompts
  • image_to_image - Transform images with text guidance
  • image_to_text - Extract text from images (OCR)
  • image_remove_background - Remove background from images
  • image_upscale - Upscale images to higher resolution
  • text_to_image_price - Calculate image generation cost
  • image_to_image_price - Calculate image transformation cost
  • image_to_text_price - Calculate OCR cost
  • image_remove_background_price - Calculate background removal cost
  • image_upscale_price - Calculate upscaling cost
Video Tools
  • text_to_video - Generate videos from text prompts
  • image_to_video - Animate static images into videos
  • audio_to_video - Generate video conditioned on audio content
  • video_description - Describe a video in text (from an uploaded file or a video URL)
  • video_upscale - Upscale a video to higher resolution
  • text_to_video_price - Calculate text-to-video cost
  • image_to_video_price - Calculate image-to-video cost
  • audio_to_video_price - Calculate audio-to-video cost
  • video_description_price - Calculate video description cost
  • video_upscale_price - Calculate video upscaling cost
Embedding Tools
  • text_to_embedding - Generate text embeddings for semantic search
  • text_to_embedding_price - Calculate embedding cost
Prompt Tools
  • prompt_booster - Enhance a prompt for any deAPI inference type using AI guides (synchronous, returns refined prompt directly)
  • prompt_booster_price - Calculate prompt-enhancement cost
Utility Tools
  • get_balance - Check account balance
  • get_available_models - List available AI models with specifications
  • check_job_status - Query async job status by ID; with wait_seconds it waits for the job and returns the finished result in the original tool's shape. A failed job reports success: false with error_reason, error_code, retryable and refunded
Webhooks

Every generation tool accepts webhook_url (HTTPS) and an optional webhook_secret (32–255 chars). deAPI calls that URL when the job finishes, signed with X-DeAPI-Signature: sha256= . ")>; the tool still returns the result to the caller as usual.

Model-driven defaults

Generation parameters such as width, height, steps, frames and fps default to the selected model's own values when left unset — each model has different limits, so there is no fixed default that works everywhere. The get_available_models output and every tool description list each model's accepted ranges. Set enhance_prompt: true on text_to_image/image_to_image to let deAPI rewrite the prompt; the result then includes prompt_boost with the prompt actually used.

Installation

Prerequisites

For running the MCP server:

  • Python 3.11 or higher
  • uv, pip, or conda for package management

For a deAPI account:

  • Sign up at deapi.ai and get your API token
Setup
  1. Clone the repository:
git clone https://github.com/deapi-ai/mcp-server-deapi.git

cd mcp-server-deapi

  1. Choose your Python environment setup:

Option A: Using `uv` (recommended - fastest)

uv pip install -e .

Option B: Using `pip`

pip install -e .

Option C: Using `conda`

# Create conda environment

conda create -n mcp-server-deapi python=3.11 conda activate mcp-server-deapi

Install dependencies

pip install -e .

  1. (Optional) Create a .env file for configuration:
# Copy the example file

cp .env.example .env

Edit with your preferences (optional - defaults work fine locally).

Set DEAPI_JWT_SECRET_KEY before exposing the server beyond localhost.

DEAPI_API_BASE_URL=https://api.deapi.ai

DEAPI_HTTP_TIMEOUT=30.0

DEAPI_MAX_RETRIES=3

Usage

Running the Server

The server can run in two modes:

Local Mode (for use with Claude Desktop on the same machine):

MCP_HOST=127.0.0.1 python -m deapi_mcp.server_remote

The server starts on http://localhost:8000. On loopback it may run without DEAPI_JWT_SECRET_KEY (it then signs OAuth tokens with a per-process key, so a restart logs clients out).

Remote Mode (for deployment to a remote server):

# A fixed signing key is required whenever the server binds beyond loopback

(the default host is 0.0.0.0) or uses Redis; without it the server refuses to start.

export DEAPI_JWT_SECRET_KEY=$(python -c "import secrets; print(secrets.token_urlsafe(32))") MCP_HOST=0.0.0.0 MCP_PORT=8000 python -m deapi_mcp.server_remote

See the Remote Deployment section for production deployment options.

Connecting from Claude Desktop / Claude.ai
Option 1: Add Connector (Recommended)

Both Claude Desktop and Claude.ai support MCP connectors with built-in OAuth authentication.

  1. Get your deAPI token from deapi.ai
  2. In Claude Desktop or Claude.ai, go to Settings → Connectors → Add Connector
  3. Fill in the connector details:
Name:              deAPI

Remote MCP server: https://your-server-domain:8000/mcp

▼ Advanced settings OAuth Client ID: deapi-mcp OAuth Client Secret: YOUR_DEAPI_TOKEN

  1. Click Add — Claude will automatically authenticate via OAuth and discover all tools.

For details on the OAuth flow, see AUTH.md.


Option 2: Config File with Bearer Token (Local Development)

Best for: Server running on the same machine, quick setup without OAuth.

Edit your Claude Desktop config file:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json
{

"mcpServers": { "deapi": { "url": "http://localhost:8000/mcp", "headers": { "Authorization": "Bearer YOUR_DEAPI_TOKEN" } } } }

Replace YOUR_DEAPI_TOKEN with your actual deAPI token. Save the file and restart Claude Desktop.

Using the Tools

Authentication is handled at the connection level, not per-tool-call. Tools do NOT accept a deapi_api_token parameter.

Here's an example workflow:

  1. Get available models:
Use get_available_models to see available models
  1. Check your balance:
Use get_balance to check remaining credits
  1. Generate an image:
Use text_to_image with:
  • prompt: "A beautiful sunset over mountains"
  • model: "Flux1schnell"
  1. Transcribe audio:
Use audio_transcription with:
  • audio: "base64-encoded-audio-or-url"
  • include_ts: true

Note: When calling tools via Claude Desktop or MCP SDK, authentication is handled automatically through the server connection (OAuth or HTTP headers). See AUTH.md for detailed OAuth setup.

Architecture

Key Components
  • DeapiClient (deapi_mcp/deapi_client.py): HTTP client with auth forwarding, built on the deAPI Python SDK transport
  • PollingManager (deapi_mcp/polling_manager.py): Smart adaptive polling for async jobs
  • Schemas (deapi_mcp/schemas.py): Pydantic models for type safety
  • Auth code store (deapi_mcp/auth_code_store.py): single-use OAuth authorization codes, in Redis when DEAPI_REDIS_HOST is set (so any replica can finish a login), otherwise in process memory
  • Webhooks (deapi_mcp/webhooks.py): optional HMAC-verified receiver for deAPI completion callbacks, so waiting tools wake on the callback instead of polling (DEAPI_WEBHOOKS_INTERNAL)
  • Tools (deapi_mcp/tools/): Organized tool implementations
  • audio.py - Audio transcription, TTS & music generation tools
  • image.py - Image generation, transformation, OCR, background removal & upscaling
  • video.py - Video generation, image/audio-to-video, description & upscale tools
  • embedding.py - Text embedding tools
  • prompt.py - Prompt booster tools
  • utility.py - Balance, models, status tools
Smart Adaptive Polling

The server uses job-type-specific polling strategies:

Job TypeInitial DelayMax DelayTimeout
Audio1s5s5 min
Image2s8s5 min
Video5s30s15 min
Embedding0.5s3s2 min
Other (incl. check_job_status)2s10s5 min

Polling uses exponential backoff with a configurable multiplier (default: 1.5x, DEAPI_POLLING_ __BACKOFF_FACTOR).

Error Handling
  • HTTP Errors: Reads are retried (3 attempts) with exponential backoff; a job submission is retried only when it provably was not processed (rate limited, or the connection never opened), so a job is never submitted twice
  • Long jobs: one tool call waits at most DEAPI_TOOL_MAX_WAIT (45 s, below the 60 s call timeout many MCP clients use). A job still running then returns success: true, done: false with its job_id, and check_job_status(job_id, wait_seconds=...) resumes the wait
  • Job Failures: Reported with the API's reason, error code, and whether the job is retryable / was refunded
  • API Errors: Properly formatted error responses

Configuration

Configuration can be set via environment variables (prefixed with DEAPI_). A variable set to an empty value (DEAPI_REDIS_PORT=) counts as unset and takes the default. Comments go on their own line: some tools (docker --env-file, kubectl create configmap --from-env-file) keep text after a value, # ... included, as part of that value. .env.example lists every variable with its default; the block below shows the common ones.

# API Configuration

DEAPI_API_BASE_URL=https://api.deapi.ai DEAPI_API_VERSION=v2

HTTP Client

DEAPI_HTTP_TIMEOUT=30.0 DEAPI_MAX_RETRIES=3

Seconds one tool call waits before returning done=false and the job_id

DEAPI_TOOL_MAX_WAIT=45.0

Polling Configuration (override defaults)

DEAPI_POLLING

只显示了开头部分;其余内容请在仓库中阅读

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