What can you do with the Google Vertex AI integration?

LiveContext exposes 12 operations on Google Vertex AI, covering cached content, model, operation and prediction. Common uses include generating video with Veo on Vertex, embedding documents for a vector index and waiting for a video generation to finish.

LiveContext

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Google Vertex AI integration

Google Cloud Vertex AI v1 for generative models and deployed endpoints: generate content with Gemini, count tokens before spending them, run prediction on publisher or custom models, launch and poll long-running generation jobs, query a vector index, and browse the model garden. The region is part of the host, so the credential carries the project id and the location.

12 toolsOAuthAPI docs

Connect Google Vertex AI How integrations work

Common Google Vertex AI use cases

generating video with Veo on Vertex
embedding documents for a vector index
waiting for a video generation to finish
scoring records against an in-house model
finding the resource name of a tuned model
discovering which Gemini versions a region serves
retrieval-augmented generation over a Vertex index
answering many questions over the same long document
rejecting prompts that would exceed the context window
resolving an endpoint id before calling a custom model

What you can do with Google Vertex AI

Each of these is one operation on one endpoint, available both as a workflow step and as a tool an AI agent can call on its own. Nothing bundles several operations into one action, so what a run did stays readable afterwards.

Most endpoints run on the free plan. A few, mainly the ones that publish on your behalf, need a paid plan.

Cached content, 2 operations

POSTcreate_cached_content
Creates a Vertex AI context cache so a large prompt prefix is billed once.
DELETEdelete_cached_content
Deletes a Vertex AI context cache before it expires.

Model, 2 operations

GETlist_models
Lists the custom and tuned models in the Vertex AI Model Registry.
GETlist_publisher_models
Lists the Google publisher models offered by Vertex AI.

Operation, 2 operations

GETget_operation
Polls a Vertex AI long-running operation until it completes.
POSTpredict_long_running
Starts a long-running Vertex AI prediction and returns the operation name to poll.

Prediction, 2 operations

POSTendpoint_predict
Runs a prediction against a custom model deployed on a Vertex AI endpoint.
POSTpredict
Runs a Vertex AI publisher model prediction, for embeddings or image generation.

Content, 1 operation

POSTgenerate_content
Generates a Gemini response on Vertex AI from a conversation and optional tools.

Endpoint, 1 operation

GETlist_endpoints
Lists Vertex AI prediction endpoints and the models deployed on them.

Neighbor, 1 operation

POSTfind_neighbors
Searches a Vertex AI Vector Search index for the nearest neighbours of a query vector.

Token, 1 operation

POSTcount_tokens
Counts the tokens of a Vertex AI prompt before sending it for generation.

Use Google Vertex AI from Claude Code, Cursor or Codex

Connect Google Vertex AI in LiveContext, create an API key in Settings, then add the LiveContext MCP server to your client. Its Google Vertex AI operations become tools the client can call, next to every other integration you connected.

Claude Code

claude mcp add --transport http livecontext https://livecontext.ai/mcp --header "X-API-Key: YOUR_API_KEY"

Codex (~/.codex/config.toml)

[mcp_servers.livecontext]
url = "https://livecontext.ai/mcp"

[mcp_servers.livecontext.http_headers]
"X-API-Key" = "YOUR_API_KEY"

Cursor, Claude Desktop and other JSON configs

{
  "mcpServers": {
    "livecontext": {
      "type": "http",
      "url": "https://livecontext.ai/mcp",
      "headers": {
        "X-API-Key": "YOUR_API_KEY"
      }
    }
  }
}

Set up the MCP server step by step

How to connect Google Vertex AI

Create your workspace
Sign up free. Nothing to install.
Connect Google Vertex AI once
Add your Google Vertex AI connection in Settings. Every tool from this integration reuses it, and secrets are encrypted at rest and visible only to you.
Describe the job in chat
The workflow builds itself in front of you and picks the endpoints it needs. Drag the steps yourself on the canvas whenever you would rather be explicit.

Google Vertex AI integration FAQ

What can you do with the Google Vertex AI integration?: LiveContext exposes 12 operations on Google Vertex AI, covering cached content, model, operation and prediction. Common uses include generating video with Veo on Vertex, embedding documents for a vector index and waiting for a video generation to finish.