How to Generate Videos from Claude Desktop, VS Code, and Cursor with Veo MCP

You can generate videos from Claude Desktop, VS Code, or Cursor by connecting the client to Ace Data Cloud’s VeoMCP server. The hosted setup uses the endpoint https://veo.mcp.acedata.cloud/mcp and an Ace Data Cloud Bearer API token. Add the server configuration for your client, then ask it to create a video from a text prompt or reference image; generation runs as a task that you can check and retrieve when ready.

What Veo MCP does

Ace Data Cloud’s VeoMCP is an MCP bridge to video-generation tools exposed through its API. It is not a video model running inside Claude, VS Code, or Cursor: the client sends requests to the configured server, which exposes Veo actions. The documented tools include text-to-video and image-to-video generation, task and batch-task status checks, model and action listings, prompt guidance, and retrieval of a 1080p result. See the project repository.

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The usual sequence is to configure the server, ask the client to submit a generation request, check the asynchronous task, then retrieve the result or request 1080p retrieval when available. The vendor setup guide describes this submit-check-retrieve workflow.

Choose hosted or local setup

The hosted route avoids installing and running the MCP server locally. The local route uses the documented Python package, installable with pip install mcp-veo or invokable with uvx; package and client instructions can change, so consult the repository before using it. For local Claude Desktop configuration, the repository shows a command-based entry with the token supplied through the environment. These are two deployment choices for Ace Data Cloud’s service, not different Veo products.

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Get a token and protect it

The hosted endpoint requires an Ace Data Cloud API token, obtained through the platform or its API documentation. Treat it like a password: do not paste it into chat prompts, commit it to a project repository, or share a configuration file containing it. VS Code’s extension uses SecretStorage for the key; the manual JSON examples place a Bearer token in configuration, so keep those files private and out of source control.

Connect Claude Desktop to the hosted server

1. Open Claude Desktop’s configuration file. On macOS, the documented path is ~/Library/Application Support/Claude/claude_desktop_config.json; on Windows, the vendor guide lists %APPDATA%Claudeclaude_desktop_config.json.

2. Add a server entry under the top-level mcpServers object. Use the hosted URL, set type to streamable-http, and pass the token in an Authorization Bearer header. The repository’s example has this shape:

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   {
     "mcpServers": {
       "veo": {
         "url": "https://veo.mcp.acedata.cloud/mcp",
         "type": "streamable-http",
         "headers": {
           "Authorization": "Bearer YOUR_API_TOKEN"
         }
       }
     }
   }

   Replace YOUR_API_TOKEN with your token; do not leave the example placeholder in a working configuration.

3. Save the file and restart Claude Desktop so it can load the server. Check the client’s MCP tools or connection status before requesting a video.

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Connect Cursor to the hosted server

1. In the project where you want Veo available, create or edit .cursor/mcp.json.

2. Use the mcpServers root object, the hosted endpoint, and a Bearer Authorization header:

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   {
     "mcpServers": {
       "veo": {
         "url": "https://veo.mcp.acedata.cloud/mcp",
         "type": "streamable-http",
         "headers": {
           "Authorization": "Bearer YOUR_API_TOKEN"
         }
       }
     }
   }

3. Save the file and allow Cursor to load the server. Confirm that its Veo tools are available before starting generation.

Connect VS Code to the hosted server

VS Code’s configuration uses a different JSON root from the Claude Desktop and Cursor examples: the server belongs under servers, in .vscode/mcp.json. The repository gives a manual JSON configuration; VS Code also has an Ace Data Cloud extension that prompts for an API key and stores it through VS Code SecretStorage.

Manual configuration

1. Create or edit .vscode/mcp.json in your workspace.

2. Put the endpoint and Bearer header under servers. The VS Code example follows this structure:

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   {
     "servers": {
       "veo": {
         "type": "http",
         "url": "https://veo.mcp.acedata.cloud/mcp",
         "headers": {
           "Authorization": "Bearer YOUR_API_TOKEN"
         }
       }
     }
   }

   Use the VS Code configuration format shown in the repository; unlike the other two clients’ examples, its root key is servers.

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3. Save the file and follow VS Code’s MCP prompt to start or trust the server as appropriate. The official VS Code MCP documentation explains workspace configuration and trust behavior.

Extension option

You can instead install the Veo MCP extension from the Visual Studio Marketplace. Its listing says it prompts for an API key and stores it with VS Code SecretStorage, avoiding manual placement of the key in the workspace JSON file.

Request and retrieve a video

1. Open a chat in the client where the Veo tools are connected. Ask for a text-to-video generation, or provide a reference image for image-to-video generation. Be explicit about the subject, action, setting, visual style, and any constraints that matter to your result.

2. Let the client call the appropriate Veo generation tool. The repository names the text and image tools veo_text_to_video and veo_image_to_video.

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3. Because generation is asynchronous, check the returned task rather than assuming the video is immediately ready. The server exposes veo_get_task and veo_get_tasks_batch for status checks.

4. When the task is complete, retrieve the result through the available tool flow. For 1080p retrieval, the documented tool is veo_get_1080p; availability depends on the task and service response.

Security and troubleshooting

• Server does not connect: Check that the URL is exactly https://veo.mcp.acedata.cloud/mcp, the client uses the correct configuration file and JSON root, and the token is a valid Bearer token. Restart or reload the client after editing configuration.

• Tools are missing: Verify that the client accepted and loaded the configuration. In VS Code, check the MCP server entry and workspace trust prompts; the official documentation notes that local MCP servers can run code on your machine and advises reviewing configurations and repository trust.

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• Task has not returned a video yet: Generation uses an asynchronous task. Query its status with the task tools and retrieve the result after completion rather than treating the submission response as the finished video.

• Keep local setup distinct: A local server runs a command on your machine and passes the token as an environment variable; the hosted setup calls the remote endpoint with an Authorization header. Do not put credentials into prompts or committed files.

Do not mix this setup with other Veo MCP projects

Other projects with similar names use different providers, credentials, and requirements. For example, the separate waimakers community project documents a Google AI Studio API key, Node.js 18 or later, Veo enablement, and billing. Those are that project’s stated prerequisites, not requirements for Ace Data Cloud’s hosted endpoint. Follow the instructions for the specific repository you choose rather than copying credentials or setup steps across projects.

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