What can you do with the Google Gemini integration?

LiveContext exposes 27 operations on Google Gemini, covering tunedmodel, cachedcontent, generation and file. Common uses include Verify file upload status, Audit uploaded media files and Build semantic search engines.

LiveContext

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Google Gemini integration

Google's multimodal AI model API for text, image, audio, and code generation. Generate content with Gemini models, create embeddings for semantic search, stream responses in real-time, count tokens for cost estimation, cache context for efficiency, fine-tune models on custom data, and manage uploaded files. Supports advanced generation controls, safety settings, structured output, function calling, grounded answers, and batch processing. Perfect for building AI-powered applications, chatbots, content generation tools, semantic search systems, and agentic workflows.

27 toolsAPI keyAPI docs

Connect Google Gemini How integrations work

Common Google Gemini use cases

Verify file upload status
Audit uploaded media files
Build semantic search engines
Monitor active cached content
Build fact-checked Q&A systems
Discover available tuned models
Remove sensitive cached content
Rename tuned models for clarity
Create domain-specific AI models
Remove deprecated model versions

What you can do with Google Gemini

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.

Tunedmodel, 6 operations

POSTcreate_tuned_model
Creates a fine-tuned model from training examples using supervised tuning.
DELETEdelete_tuned_model
Permanently deletes a fine-tuned model.
GETget_tuned_model
Retrieves detailed information about a specific fine-tuned model.
GETlist_tuned_models
Retrieves a paginated list of all fine-tuned models.
POSTtransfer_tuned_model_ownership
Transfers ownership of a tuned model to another user.
PATCHupdate_tuned_model
Updates properties of a fine-tuned model such as display name or description.

Cachedcontent, 5 operations

POSTcreate_cached_content
Creates cached content for efficient reuse across multiple generation requests.
DELETEdelete_cached_content
Permanently deletes a cached content entry before its automatic expiration.
GETget_cached_content
Retrieves detailed information about a specific cached content entry.
GETlist_cached_contents
Retrieves a paginated list of all cached content entries.
PATCHupdate_cached_content
Updates the TTL or expiration time of an existing cached content entry.

Generation, 5 operations

POSTbatch_generate_content
Processes multiple content generation requests in a single batch operation.
POSTgenerate_content
Generates AI-powered content using Google Gemini multimodal models with customizable parameters and safety settings.
POSTgenerate_content_tuned
Generates content using a fine-tuned model with customized behavior.
POSTstream_generate_content
Streams generated content in real-time as tokens are produced, enabling responsive user experiences.
POSTstream_generate_content_tuned
Streams generated content from a fine-tuned model in real-time as tokens are produced.

File, 4 operations

DELETEdelete_file
Permanently deletes an uploaded file before its automatic expiration.
GETget_file
Retrieves detailed metadata for a specific uploaded file.
GETlist_files
Retrieves a paginated list of all uploaded files with their metadata.
POSTupload_file
Uploads a media file for use with multimodal Gemini models and returns file metadata.

Embedding, 2 operations

POSTbatch_embed_contents
Efficiently generates vector embeddings for multiple text contents in a single API request.
POSTembed_content
Generates vector embeddings from text content for semantic search and similarity tasks.

Model, 2 operations

GETget_model
Retrieves detailed specifications and capabilities for a specific Gemini model.
GETlist_models
Retrieves a paginated list of all available Gemini models with their capabilities and specifications.

Answer, 1 operation

POSTgenerate_answer
Generates factual answers with source attribution, ideal for grounded question-answering applications.

Prediction, 1 operation

POSTpredict
Generates predictions using the specified model with configurable parameters.

Token, 1 operation

POSTcount_tokens
Calculates the number of tokens in provided content before making generation requests.

Use Google Gemini from Claude Code, Cursor or Codex

Connect Google Gemini in LiveContext, create an API key in Settings, then add the LiveContext MCP server to your client. Its Google Gemini 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 Gemini

Create your workspace
Sign up free. Nothing to install.
Connect Google Gemini once
Add your Google Gemini 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 Gemini integration FAQ

What can you do with the Google Gemini integration?: LiveContext exposes 27 operations on Google Gemini, covering tunedmodel, cachedcontent, generation and file. Common uses include Verify file upload status, Audit uploaded media files and Build semantic search engines.