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Placid.app MCP Server

Placid.app MCP Server

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github.com/feloresMedia & Design15Updated 83w ago

About Placid.app MCP Server

Placid.app MCP Server connects MCP-compatible hosts to Placid.app for template discovery and image or video generation with dynamic content. It is intended for workflows that create visual assets from existing Placid templates.

Key features

  1. 01Filter and list templates
  2. 02Generate images from templates
  3. 03Generate videos from templates
  4. 04Supply dynamic text and media layers
  5. 05Configure video audio and trimming

Use cases

  1. 01Find templates by collection or tags
  2. 02Create images with customized text and visuals
  3. 03Produce videos with dynamic media and text

MCP support

Exposes Placid.app template listing and image and video generation as tools for MCP clients. It uses a Placid API token to access the service.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"placid": {
"command": "npx",
"args": [
"-y",
"@smithery/cli"
]
}
}
}
Run commandnpx -y @smithery/cli

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npm@smithery/cli

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Placid.app MCP Server

smithery badge

An MCP server implementation for integrating with Placid.app's API. This server provides tools for listing templates and generating images and videos through the Model Context Protocol.

README image

Features

  • List available Placid templates with filtering options
  • Generate images and videos using templates and dynamic content
  • Secure API token management
  • Error handling and validation
  • Type-safe implementation

Requirements: Node.js

  1. Install Node.js (version 18 or higher) and npm from nodejs.org
  2. Verify installation:
    node --version
    npm --version
    

Installation

Quick Start (Recommended)

The easiest way to get started is using Smithery, which will automatically configure everything for you:

npx -y @smithery/cli install @felores/placid-mcp-server --client claude
Manual Configuration

If you prefer to configure manually, add this to your Claude Desktop or Cline settings:

{
  "mcpServers": {
    "placid": {
      "command": "npx",
      "args": ["@felores/placid-mcp-server"],
      "env": {
        "PLACID_API_TOKEN": "your-api-token"
      }
    }
  }
}

Getting Your Placid API Token

  1. Log in to your Placid.app account
  2. Go to Settings > API
  3. Click on "Create API Token"
  4. Give your token a name (e.g., "MCP Server")
  5. Copy the generated token
  6. Add the token to your configuration as shown above

Development

# Run in development mode with hot reload
npm run dev

# Run tests
npm test

Tools

placid_list_templates

Lists available Placid templates with filtering options. Each template includes its title, ID, preview image URL, available layers, and tags.

Parameters
  • collection_id (optional): Filter templates by collection ID
  • custom_data (optional): Filter by custom reference data
  • tags (optional): Array of tags to filter templates by
Response

Returns an array of templates, each containing:

  • uuid: Unique identifier for the template
  • title: Template name
  • thumbnail: Preview image URL (if available)
  • layers: Array of available layers with their names and types
  • tags: Array of template tags
placid_generate_video

Generate videos by combining Placid templates with dynamic content like videos, images, and text. For longer videos (>60 seconds processing time), you'll receive a job ID to check status in your Placid dashboard.

Parameters
  • template_id (required): UUID of the template to use
  • layers (required): Object containing dynamic content for template layers
    • For video layers: { "layerName": { "video": "https://video-url.com" } }
    • For image layers: { "layerName": { "image": "https://image-url.com" } }
    • For text layers: { "layerName": { "text": "Your content" } }
  • audio (optional): URL to an mp3 audio file
  • audio_duration (optional): Set to 'auto' to trim audio to video length
  • audio_trim_start (optional): Timestamp of trim start point (e.g. '00:00:45' or '00:00:45.25')
  • audio_trim_end (optional): Timestamp of trim end point (e.g. '00:00:55' or '00:00:55.25')
Response

Returns an object containing:

  • status: Current status ("finished", "queued", or "error")
  • video_url: URL to download the generated video (when status is "finished")
  • job_id: ID for checking status in Placid dashboard (for longer videos)
Example Usage for LLM models
{
  "template_id": "template-uuid",
  "layers": {
    "MEDIA": { "video": "https://example.com/video.mp4" },
    "PHOTO": { "image": "https://example.com/photo.jpg" },
    "LOGO": { "image": "https://example.com/logo.png" },
    "HEADLINE": { "text": "My Video Title" }
  },
  "audio": "https://example.com/background.mp3",
  "audio_duration": "auto"
}
placid_generate_image

Generate static images by combining Placid templates with dynamic content like text and images.

Parameters
  • template_id (required): UUID of the template to use
  • layers (required): Object containing dynamic content for template layers
    • For text layers: { "layerName": { "text": "Your content" } }
    • For image layers: { "layerName": { "image": "https://image-url.com" } }
Response

Returns an object containing:

  • status: "finished" when complete
  • image_url: URL to download the generated image
Example Usage for LLM models
{
  "template_id": "template-uuid",
  "layers": {
    "headline": { "text": "Welcome to My App" },
    "background": { "image": "https://example.com/bg.jpg" }
  }
}

Documentation

For more detailed information about the Placid API, visit the Placid API Documentation.

License

MIT

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.