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# Magic Hour MCP Server

OpenAPI-backed MCP server for Magic Hour image, video, and audio generation.

At startup, this server reads `docs/openapi.json` and builds MCP tools with
`FastMCP.from_openapi()`. The OpenAPI spec supplies endpoint coverage, while
Magic Hour MCP policies add agent-facing guidance for async polling, uploads,
and project downloads.

Docs:

- [Magic Hour agent skills](https://github.com/magichourhq/skills) - media workflows, published examples, and recovery guidance
- `user.md` - hosted endpoint user guide
- `integration-handoff.md` - FastAPI mount checklist
- `docs/detailed-step-by-step-integration.md` - full backend integration guide
- `docs/api-reference.md` - generated API reference

## Setup

```sh
pip install -e .
```

## Run locally

```sh
python main.py
```

Local MCP endpoint:

```text
http://127.0.0.1:8000/
```

This local dev server runs at `/`, not `/mcp`. The host app adds `/mcp` when it mounts the server.

By default, requests go to the production Magic Hour API:

```text
https://api.magichour.ai
```

Tool discovery is public. Tool calls must include your Magic Hour API key:

```text
Authorization: Bearer <magic_hour_api_key>
```

Agents can discover the hosted server card at:

```text
https://mcp.magichour.ai/.well-known/mcp/server-card.json
```

Environment variables:

```sh
MAGIC_HOUR_API_BASE_URL=https://api.magichour.ai
MAGIC_HOUR_OPENAPI_PATH=docs/openapi.json
MCP_OAUTH_ISSUER_URL=https://mcp.magichour.ai
MCP_OAUTH_RESOURCE_URL=https://mcp.magichour.ai
```

Override `MAGIC_HOUR_API_BASE_URL` to use a mock or another API base:

```sh
MAGIC_HOUR_API_BASE_URL=https://api.sideko.dev/v1/mock/magichour/magic-hour/latest python main.py
```

## OAuth

This server is an OAuth protected resource only. The authorization server is the Magic Hour
web app (`https://magichour.ai`): users sign in and consent there, and it returns a Magic Hour
API key as the access token, which this server passes through to the API. Production requires
`MCP_OAUTH_ISSUER_URL` and `MCP_OAUTH_RESOURCE_URL`. See `docs/future-oauth-support.md`.

## Test with MCP Inspector

1. Start the server.
2. Run:
   ```sh
   npx @modelcontextprotocol/inspector
   ```
3. In Inspector:
   - Transport: `Streamable HTTP`
   - URL: `http://127.0.0.1:8000/`
   - Header: `Authorization: Bearer <magic_hour_api_key>`
4. Call `ping`.
5. Call `video_assets_generate_presigned_url` or another generated tool.

Notes:

- FastMCP generates endpoint tools from OpenAPI at startup.
- Creation tools return `id` and `credits_charged` immediately.
- OpenAPI `operationId` values are normalized to descriptive snake_case tool names.
- The shared `/v1/files/upload-urls` endpoint is named `video_assets_generate_presigned_url`. It accepts `video`, `audio`, and `image` items.
- Use `wait_for_*_project` to poll jobs. Use `exact_download_urls` exactly as
  returned; never append expiration metadata.
- Image and audio wait tools also return inline media when supported.

Rebuild and type-check the MCP App UI with `cd web && npm ci && npm run build`.

## File uploads

Magic Hour does not accept raw file bytes inside tool arguments. The flow is:

1. Call the generated shared upload-URL tool, `video_assets_generate_presigned_url`
2. Upload the file bytes to the returned `upload_url`
3. Pass the returned `file_path` into the generated creation tool

Direct public media URLs may work, but uploaded `file_path` values are more
reliable. Upload bytes from the caller or a dedicated upload bridge; the hosted
MCP server never reads caller-supplied local filesystem paths. Browser chat needs
a separate upload UI or bridge; see `docs/future-chat-ui-handoff.md`.

## MCP voice presets

AI Voice Generator exposes and accepts only exact API `voice_name` strings in
`APPROVED_VOICE_NAMES` in `mcp_magichour/openapi_policies.py`. Populate that tuple
with verified approved generic/non-celebrity presets to enable the tool on the
next deployment. No existing upstream voice is assumed safe. An empty allowlist
omits Voice Generator from tool discovery and calls. Voice Cloner is always
excluded from MCP.

The MCP policy removes upstream voice descriptions, defaults, examples and SDK
snippets at startup and when regenerating `docs/api-reference.md`. Daily OpenAPI
syncs cannot expand the allowlist. Do not edit `docs/openapi.json` to set MCP
voices: it remains the unmodified upstream API snapshot. The main Magic Hour
product and the public API are unchanged.

MCP Meme Generator exposes only the generic `Random` template option, keeping
public-figure template names out of its schema.

TDQS

A3.9/5.0

Scored across 45 tools

Disambiguation4/5

Each tool targets a distinct media generation task (e.g., text_to_video vs image_to_video) or lifecycle operation (create vs retrieve vs delete). Some related tools like face_swap_photo_create_image, head_swap_create_image, and body_swap_create_image are similar but descriptions clarify their specific inputs. The main risk is the large number of video/image creation tools, but the descriptions are distinct enough.

Naming Consistency2/5

Naming follows multiple inconsistent patterns: some use 'ai_' prefixes (ai_image_generator_create_image), some use domain prefixes (video_projects_retrieve_details), some use feature-based prefixes (text_to_video_create_video), and a few are standalone (ping, saved_items_list). The verb_noun structure varies (e.g., 'create_video' vs 'retrieve_details' vs 'delete'). This lack of a uniform convention makes it harder for an agent to predict tool names.

Tool Count3/5

With 45 tools, this is on the heavy side. The server covers a wide range of media generation features (video, image, audio) plus lifecycle utilities (upload, create, poll, wait, delete, fetch), which justifies many tools, but the count is high and near the threshold where agents may struggle to choose. It's borderline appropriate but not excessively bloated.

Completeness4/5

The server provides a complete lifecycle for each media type: presigned upload URLs, creation tools, retrieval of project details, wait helpers, download fetching, and deletion. There are also utilities for saved items, account info, and face detection. Minor gaps include no explicit list-projects tool (only saved items) and no cancel/update operations, but these are not essential for the core generation workflow.

Maintenance

ActivityActive
ResponsivenessUnresponsive