deAPI MCP Server

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=<HMAC-SHA256(secret, "<timestamp>.<body>")>; 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 app.deapi.ai and create an API token at app.deapi.ai/settings/api-keys

Setup

1. Clone the repository:

git clone https://github.com/deapi-ai/mcp-server-deapi.gitcd mcp-server-deapi

2. 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 environmentconda create -n mcp-server-deapi python=3.11conda activate mcp-server-deapi
# Install dependenciespip install -e .

3. (Optional) Create a .env file for configuration:

# Copy the example filecp .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 app.deapi.ai/settings/api-keys
2. In Claude Desktop or Claude.ai, go to Settings → Connectors → Add Connector
3. Fill in the connector details:

Name:              deAPIRemote MCP server: https://your-server-domain:8000/mcp
▼ Advanced settingsOAuth Client ID:     deapi-mcpOAuth Client Secret: YOUR_DEAPI_TOKEN

4. 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

2. Check your balance:

Use get_balance to check remaining credits

3. Generate an image:

Use text_to_image with:- prompt: "A beautiful sunset over mountains"- model: "Flux1schnell"

4. 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:

Polling uses exponential backoff with a configurable multiplier (default: 1.5x, DEAPI_POLLING_<TYPE>__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 ConfigurationDEAPI_API_BASE_URL=https://api.deapi.aiDEAPI_API_VERSION=v2
# HTTP ClientDEAPI_HTTP_TIMEOUT=30.0DEAPI_MAX_RETRIES=3
# Seconds one tool call waits before returning done=false and the job_idDEAPI_TOOL_MAX_WAIT=45.0
# Polling Configuration (override defaults)DEAPI_POLLING_AUDIO__INITIAL_DELAY=1.0DEAPI_POLLING_AUDIO__MAX_DELAY=5.0DEAPI_POLLING_AUDIO__TIMEOUT=300.0
# Internal completion webhooks (off by default). The receiver URL must be# reachable from api.deapi.ai; a half-configured set refuses to start.# Uncomment and fill in all three to turn them on.DEAPI_WEBHOOKS_INTERNAL=false# DEAPI_WEBHOOK_RECEIVER_URL=https://mcp.example.com/webhooks/deapi# Secret the API signs callbacks with: 32-255 characters# DEAPI_WEBHOOK_SECRET=DEAPI_WEBHOOK_FALLBACK_POLL_INTERVAL=15.0
# Shared store for OAuth authorization codes (optional). Unset host = in-memory# store, which only works with a single replica. Uncomment and set your own host.# DEAPI_REDIS_HOST=redis.example.comDEAPI_REDIS_PORT=6379# DEAPI_REDIS_PASSWORD=DEAPI_REDIS_DB=0# Connection scheme: plain | tls (tls needs DEAPI_REDIS_CA_FILE)DEAPI_REDIS_SCHEME=plain# true = refuse to start without a Redis hostDEAPI_REDIS_REQUIRED=false
# Seconds in-flight tool calls may run after SIGTERM before they are cancelled.# Keep it below your platform's termination grace period.MCP_GRACEFUL_SHUTDOWN_SECONDS=930
# Behind a reverse proxy: the public URL OAuth discovery advertises (without it the# 401 challenge points at the bind address), and which proxies' X-Forwarded-* headers# are trusted.# PUBLIC_BASE_URL=https://mcp.example.com# FORWARDED_ALLOW_IPS=*

With internal webhooks on, every job the server submits carries its own receiver URL and the API calls back the moment the job ends; the waiting tool wakes at once, with a slow poll underneath as a safety net. A caller-supplied webhook_url takes precedence for that job (the API takes one webhook per job), which then polls normally. A replayed delivery (same delivery_id) wakes nobody twice. The job registry is in process memory by default; with several replicas, see DEPLOYMENT.md.

Development

Project Structure

mcp-server-deapi/├── deapi_mcp/│   ├── server_remote.py       # Streamable-HTTP MCP server│   ├── deapi_client.py        # HTTP client with auth forwarding│   ├── polling_manager.py     # Smart adaptive polling logic│   ├── schemas.py             # Pydantic models│   ├── config.py              # Configuration management│   ├── auth.py                # Authentication middleware│   ├── fastmcp_auth.py        # FastMCP OAuth provider│   ├── oauth_endpoints.py     # OAuth 2.0 endpoints│   ├── auth_code_store.py     # OAuth authorization codes (Redis or in-memory)│   ├── observability.py       # Per-call outcome, injectable metrics sinks│   ├── webhooks.py            # Internal completion-webhook receiver│   └── tools/                 # Tool implementations│       ├── audio.py           # Audio transcription, TTS & music generation│       ├── image.py           # Image generation, OCR & processing│       ├── video.py           # Video generation, image/audio-to-video, description & upscale│       ├── embedding.py       # Text embeddings│       ├── prompt.py          # Prompt booster│       ├── utility.py         # Balance, models, status│       ├── _price_helpers.py  # Resolve parameters from the model card│       └── _responses.py      # Shared tool response shape├── tests/                     # Test suite│   ├── __init__.py│   └── conftest.py           # Pytest fixtures├── pyproject.toml             # Dependencies├── Dockerfile                 # Container build├── docker-compose.yml         # Container orchestration├── .env.example              # Environment config template├── README.md                  # This file├── DEPLOYMENT.md              # Deployment guide├── AUTH.md                    # OAuth authentication setup└── CLAUDE.md                  # Claude Code guidance

Running Tests

Install dev dependencies:

uv pip install -e ".[dev]"

Run tests:

pytest

Run smoke tests (requires a running server):

python tests/smoke_test.py

Code Formatting

Format code with Black:

black deapi_mcp/ tests/

Lint with Ruff:

ruff check deapi_mcp/ tests/

Type-check with mypy (strict):

mypy deapi_mcp

CI runs all three plus the test suite on Python 3.11–3.14.

Building your own server on the core

deapi_mcp is importable, so a deployment can assemble its own server from the same tools and add what it needs (for example its own metrics) without forking:

from fastmcp import FastMCPfrom deapi_mcp.server_remote import create_app, register_core_tools
server = FastMCP("my-deapi")register_core_tools(server)            # every deAPI toolapp = create_app(server, metrics=None) # Starlette app: /mcp, /health, OAuth routes

As with the stock server, set DEAPI_JWT_SECRET_KEY first: without it the app refuses to start on a non-local host.

metrics is an optional overlay that instruments tool calls and OAuth and mounts its own routes; the stock server (build_app()) passes none. job_registry replaces the in-process registry that completion webhooks wake waiting tools through, for a deployment whose replicas share one receiver URL: implement deapi_mcp.webhooks.JobRegistry (or subclass JobCompletionRegistry, relay signal to the other replicas and call wake where it arrives). Its start/stop run with the app's lifespan. Recorders for per-call outcomes and rejected logins plug in through deapi_mcp.observability. Settings an overlay can use (DEAPI_METRICS_TOKEN, DEAPI_METRICS_REDIS_DB, DEAPI_METRICS_PREFIX) are already defined in deapi_mcp.config; the core itself never reads them.

The package ships type information (py.typed) and exposes deapi_mcp.__version__.

API Token Security

Important: The MCP server does NOT store API tokens. Authentication works as follows:

1. For Remote HTTP Server: Authentication is handled via OAuth 2.0 (Authorization Code with PKCE) or HTTP headers (Authorization: Bearer token)
2. Token forwarding: The server forwards authentication to the deAPI API for each request
3. No persistence: Tokens are used only for the specific request and never persisted or logged
4. Per-connection auth: Tools do NOT accept deapi_api_token parameters - authentication is managed at the connection level

Always keep your API tokens secure and never commit them to version control. See AUTH.md for detailed OAuth setup.

Remote Deployment

For production environments or when you want to host the MCP server on a remote machine, use the remote server mode.

Quick Start with Docker

1. Build and run with Docker:

docker build -t mcp-server-deapi .docker run -d -p 8000:8000 --name mcp-server-deapi   -e DEAPI_JWT_SECRET_KEY="<a long random secret>"   --stop-timeout 960   mcp-server-deapi

2. Or use Docker Compose (reads DEAPI_JWT_SECRET_KEY from the environment or .env):

docker compose up -d

3. Configure Claude Desktop to connect:

{  "mcpServers": {    "deapi": {      "url": "http://your-server-ip:8000/mcp"    }  }}

Manual Remote Deployment

1. On your remote server:

git clone https://github.com/deapi-ai/mcp-server-deapi.gitcd mcp-server-deapipip install -e .export DEAPI_JWT_SECRET_KEY="<a long random secret>"python -m deapi_mcp.server_remote

2. For production with systemd:

# Create /etc/systemd/system/mcp-server-deapi.servicesudo systemctl enable mcp-server-deapisudo systemctl start mcp-server-deapi

3. Behind a reverse proxy (nginx + SSL):

server {    listen 443 ssl http2;    server_name mcp.yourdomain.com;
    ssl_certificate /path/to/cert.pem;    ssl_certificate_key /path/to/key.pem;
    location / {        proxy_pass http://localhost:8000;        proxy_http_version 1.1;        proxy_set_header Upgrade $http_upgrade;        proxy_set_header Connection "upgrade";        proxy_buffering off;        proxy_cache off;        proxy_read_timeout 86400;    }}

Cloud Deployment Options

• Railway.app: Push to GitHub, connect repository, deploy automatically
• Fly.io: fly launch && fly deploy
• Heroku: heroku create && git push heroku main
• DigitalOcean: Use App Platform or Droplets with Docker
• AWS/GCP/Azure: Deploy with container services (ECS, Cloud Run, Container Instances)

For detailed deployment instructions, security considerations, monitoring, and troubleshooting, see DEPLOYMENT.md.

Troubleshooting

Connection Issues

If the server fails to connect:

1. Check your API token is valid
2. Verify network connectivity to api.deapi.ai
3. Check the logs for specific error messages
4. For remote servers: verify firewall rules and that port 8000 is accessible

Job Timeouts

If jobs are timing out:

1. Check your balance with get_balance
2. Verify the job type timeout is appropriate
3. Use check_job_status to check if the job is still processing

Model Not Found

If you get model errors:

1. Use get_available_models to see available models
2. Ensure you're using the correct model name
3. Check if the model supports your requested operation

Remote Connection Issues

If remote MCP connection fails:

1. Test the server: curl http://your-server:8000/health (a bare GET /mcp answers 405: the transport is stateless, POST only)
2. Check server logs: docker logs mcp-server-deapi or journalctl -u mcp-server-deapi
3. Verify firewall rules and SSL certificates (if using HTTPS)
4. Ensure MCP endpoint is accessible from your client

License

This project is licensed under the MIT License - see the LICENSE file for details.

Changelog

See CHANGELOG.md.

Support

For issues related to:

• This MCP Server: Open an issue
• A security vulnerability: do not open a public issue; see SECURITY.md
• deAPI Platform: Visit docs.deapi.ai
• MCP Protocol: Visit modelcontextprotocol.io