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  3. Vicsee MCP Server
Vicsee MCP Server logo
Health: ActiveRecent health check succeeded.Last checked 9/21/2026, 2:47:15 AM

Vicsee MCP Server

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ai-mediaimage-generationvideo-generationupscaling

Generate, edit, upscale, and retrieve AI images and videos through VicSee MCP tools.

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 CursorAdd to VS Code
Automated check passed— started and listed 8 tools correctly (Today).
Manual Client & Custom JSON ConfigExpand JSON â–¾

Client Config & Setup

Configure Environment Variables (API Keys, Tokens, Options):
Add required secrets below — values are included directly in the generated snippet so you can copy and paste with confidence.
Quick Add:
Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "vicseeai-vicsee-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "@vicsee/mcp-server"
      ],
      "env": {
        "VICSEE_API_KEY": "YOUR_VALUE_HERE"
      }
    }
  }
}

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

Install Tool Schemas (8) Directory Badge Claim listing Alternatives🎨 More in Art & Culture

Overview

The vicseeai/vicsee-mcp-server MCP server exposes VicSee models for generating, editing, and upscaling images and videos from MCP-compatible agents. It uses asynchronous tasks: generation and upscaling return an ID, which the agent polls until a result URL or error is available. Models, capabilities, and credit costs can be queried before selecting a generation model. Use it when an agent needs VicSee media creation or enhancement, provided you have a VicSee API key and paid credits.

Use cases

•Generate short AI videos from text prompts
•Create images with VicSee models
•Edit videos with supported models
•Upscale images or videos
•Upload local media for reference workflows

Key features

•List VicSee models and credit costs
•Generate images and videos asynchronously
•Poll media task status and retrieve result URLs
•Upscale images and videos
•Upload local media to public URLs
•Check VicSee credit balance

Capabilities & Tool Schemas (8) ~3.0k tokensApproximate context cost of this server’s tool schemas (~4 chars/token), before any tool is called. Actual usage depends on your client and model.Verified live Verified liveCaptured by calling this server’s live tools/list endpoint.

Inspect callable tools, capabilities, and parameters exposed to AI agents by Vicsee MCP Server.

vicsee_list_models

List available VicSee models with their capabilities and credit costs. Call this first to find a model id to pass to vicsee_generate. Optionally filter by media type.

vicsee_estimate

Return the EXACT credits a vicsee_generate call will charge, without generating anything. Takes the same arguments as vicsee_generate. The server runs the identical validation, model/resolution resolution and per-second billing (probing any reference video for its length) and stops before the charge, so `credits` is what vicsee_generate will deduct. Also returns `resolvedParams` (the settings that will actually apply, including defaults you did not send), `creditsRemaining` and `sufficient`. Call this before vicsee_generate whenever the user cares about cost, is comparing models, or is attaching a video reference (per-second billing across input + output). Invalid requests return the same error vicsee_generate would, so it also works as a free dry run.

vicsee_generate

Create an AI image or video with VicSee. Generation is ASYNCHRONOUS: this returns a task `id` immediately, then poll `vicsee_get_task` with that id until status is "completed" (the result URL appears in result.url) or "failed". Use vicsee_list_models to pick a `model` and see its valid options. For image-to-video / image-to-image, pass source images in `image_urls`. For reference-to-video models (e.g. "seedance-2-5-reference-to-video"), pass references in reference_image_urls / reference_video_urls / reference_audio_urls and refer to them positionally in the prompt as @Image1, @Image2, … For native 2K output at the best value per second use the "minimax-h3-*" models (MiniMax H3, also called Hailuo 03): 4-15s, 768P/2K, always-on stereo audio, billed per second (25 credits/s at 768P, 40 at 2K). Note "minimax-h3-image-to-video" takes the first frame in image_urls[0] and an optional last frame in image_urls[1], and has NO aspect_ratio parameter — the ratio follows the frames; "minimax-h3-reference-to-video" requires at least one image OR video reference (audio alone is rejected) and accepts at most 5 reference images. For clips longer than 15 seconds use the "seedance-2-5-*" models, which generate up to 30 seconds (480p/720p only, no 1080p or 4K); the "seedance-2-0-*" models cap at 15 seconds but add 1080p and 4K. Note that supplying a video reference switches billing to per-second across input + output, so a long reference clip costs considerably more than the duration table suggests. When cost matters, call vicsee_estimate with the same arguments FIRST — it returns the exact credits this call will charge and creates nothing. IMAGE inputs (image_urls, reference_image_urls) may be a public https URL, a local file path (this server reads and base64-encodes it for you), or a base64 data URI. VIDEO/AUDIO inputs (reference_video_urls, reference_audio_urls) must be public https URLs. For video-edit models (e.g. "happyhorse-video-edit"), pass the source clip in video_url and optionally set audio_setting ("auto" or "origin"). Input images must have an aspect ratio between 0.4 and 2.5 (width÷height) and at most 36MP — wide-strip composites (e.g. 3:1) are rejected; use a 2×2 grid layout instead. On "failed" tasks, read error.key and error.message from vicsee_get_task — they state the actual reason (do not guess).

vicsee_get_task

Poll a generation or upscale task by its id. IMPORTANT: finished media URLs are kept for 7 days only — result.expires_at is the exact time the URL stops working; download or re-host anything the user wants to keep. status is one of "pending" | "processing" | "queued" | "completed" | "failed". When "completed", the media URL is in result.url (videos/images) or result.songs (music). When "failed", details are in error. Poll every few seconds until completed or failed.

vicsee_upscale_image

Upscale a publicly accessible image (JPEG/PNG/WebP). Asynchronous: returns a task id — poll vicsee_get_task until completed. upscale_factor defaults to "2" (max "4"). Credits are tiered by OUTPUT size (input megapixels × factor²): ≤24MP = 20, ≤48MP = 40, ≤96MP = 80; outputs above 96MP are rejected.

vicsee_upscale_video

Upscale a publicly accessible video (MP4/MOV/MKV, up to 60s). Asynchronous: returns a task id — poll vicsee_get_task until completed. upscale_factor defaults to "2".

How Vicsee MCP Server works

What vicseeai/vicsee-mcp-server MCP server does

The vicseeai/vicsee-mcp-server MCP server connects an MCP client to VicSee’s AI media API. It lets an agent inspect available models, create images or videos, edit supported media, upscale images and videos, check credit usage, and upload local source files for use in later requests.

Available model families include Seedance, Veo, Kling, FLUX, and Nano Banana, subject to what VicSee returns for the configured account. The model listing includes capabilities and credit costs, so an agent can select a model and construct a compatible request before starting a job.

How it works

Generation and upscaling are asynchronous. A call to vicsee_generate, vicsee_upscale_image, or vicsee_upscale_video returns a task ID rather than waiting for the media to finish. The client should pass that ID to vicsee_get_task repeatedly until the task is completed or failed. Completed image and video tasks expose the output at result.url; failed tasks provide details in error.

For text-to-media requests, the agent supplies a prompt and model ID. Image-to-image and image-to-video requests can use image_urls. Reference-based video models accept image, video, and audio reference URL arrays, with prompt references such as @Image1 resolved by position. Video-edit models accept a source video_url and can optionally use an audio setting.

The upload tool accepts a local image, video, or audio file and returns a public HTTPS URL. This is useful for supplying large reference inputs without placing their contents directly in a tool call. Image inputs may also be local paths or base64 data URIs; reference videos and audio must use public HTTPS URLs.

Setup and configuration

Install the package through the MCP client’s stdio configuration:

config.json
{
  "command": "npx",
  "args": ["-y", "@vicsee/mcp-server"],
  "env": { "VICSEE_API_KEY": "sk-your-key-here" }
}

Create the API key in VicSee under Settings and API. VicSee API access requires a paid plan or credit pack. VICSEE_API_KEY is required. VICSEE_BASE_URL is optional and can replace the default API base URL, https://vicsee.com/api/v1.

The README identifies Claude Desktop, Claude Code, Cursor, Hermes Agent, and OpenClaw as compatible clients or agent environments. For Claude Desktop, add the server to claude_desktop_config.json; Cursor uses its MCP settings. A local development build can also run with Node after installing dependencies and building the project.

Tools and capabilities

  • vicsee_list_models lists models, media types, capabilities, and credit costs, with optional media-type filtering.
  • vicsee_generate starts image or video generation, editing, and supported reference-based workflows.
  • vicsee_get_task retrieves task status and completed media URLs.
  • vicsee_upscale_image handles public JPEG, PNG, or WebP images.
  • vicsee_upscale_video handles public MP4, MOV, or MKV videos up to 60 seconds.
  • vicsee_get_credits reports the balance for the configured API key.
  • vicsee_upload publishes a local media file and returns its public URL.

Upscaling defaults to a factor of 2 when no factor is supplied. Generation and model-specific options must follow the capabilities returned by VicSee.

Limitations and notes

Every generation consumes VicSee credits, and model costs vary. Agents should check the model list and credit balance before launching expensive jobs. The server does not make asynchronous work synchronous, so clients must implement polling and handle both completion and failure states.

Output links are served from VicSee’s CDN. Video and audio reference inputs need to be publicly reachable HTTPS URLs, while local files should be uploaded first when a public URL is required. The project is licensed under MIT.

Read the full README →View source on GitHub →

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

npm downloads
442
Package downloads in the last 30 days.
Last commit
4d ago
Most recent push to the default branch.
Availability
100%
Our rolling endpoint + install checks that succeeded.
Install check
Passed
Our sandbox started it and listed its tools.
Tools exposed
8
Callable tools this server registers over MCP.
Directory activity
4 views
Config copies, upvotes, and views on AllMCPs.

Reviews

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Frequently Asked Questions about Vicsee MCP Server

Configure the MCP client to run `npx -y @vicsee/mcp-server` and provide the `VICSEE_API_KEY` environment variable.

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Technical Specs & Signals

Category🎨Art & Culture
PricingPaid
More technical detailsExpand â–¾
TransportSTDIO
RuntimeNode.js
AuthAPI key
LicenseMIT
ClientsClaude Desktop, Cursor
Last updatedSep 17, 2026
10/11 checks healthy over the last 42d
Views4
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars0
GitHub Star CountTotal stargazers on GitHub representing community popularity (0 stars).
Last commit4d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Sep 17, 2026
npm downloads442/mo
Monthly npm DownloadsAverage monthly package installs recorded from npm registry statistics.
72Quality signal: Great · 72/100How this signal is calculated ▾
Server availability25/25
Verified ownership10/20
Documentation & tools30/30
Adoption & activity6/15
Community engagement1/10

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