Connect
Optimize
Secure
Google Gemini MCP Server
for AI Agents
Production-ready Google Gemini MCP server with 20 extensible actions — plus built-in authentication, security, and optimized execution.
Google Gemini MCP Server
Built by 
StackOne
StackOne 
Coverage
20 Agent Actions
Create, read, update, and delete across Google Gemini — and extend your agent's capabilities with custom actions.
Authentication
Agent Tool Authentication
Per-user OAuth in one call. Your Google Gemini MCP server gets session-scoped tokens with zero credentials stored on your infra.
Agent Auth →
Security
Agent Protection
Every Google Gemini tool response scanned for prompt injection in milliseconds — 88.7% accuracy, all running on CPU.
Prompt Injection Defense →
Performance
Max Agent Context. Min Cost.
Free up to 96% of your agent's context window to enhance reasoning and reduce cost, on every Google Gemini call.
Tools Discovery →What is the Google Gemini MCP Server?
A Google Gemini MCP server lets AI agents read and write Google Gemini data through the Model Context Protocol — Anthropic's open standard for connecting LLMs to external tools. StackOne's Google Gemini MCP server ships with 20 pre-built actions, fully extensible via the Connector Builder — plus managed authentication, prompt injection defense, and optimized agent context. Connect it from MCP clients like Claude Desktop, Cursor, and VS Code, or from agent frameworks like OpenAI Agents SDK, LangChain, and Vercel AI SDK.
All Google Gemini MCP Tools and Actions
Every action from Google Gemini's API, ready for your agent. Create, read, update, and delete — scoped to exactly what you need.
Cached Contents
- Create Cached Content
Cache content for reuse across multiple requests
- Get Cached Content
Retrieve cached content details and status
- List Cached Contents
List all cached content resources
- Update Cached Content
Update cached content properties (extend TTL)
- Delete Cached Content
Delete a cached content resource
Generate Contents
- Generate Content
Generate text content from a prompt using a Gemini model
Stream Generate Contents
- Stream Generate Content
Stream generated content in real-time using Server-Sent Events
Embed Contents
- Embed Content
Generate high-quality embedding vectors for text or multimodal content using Gemini embedding models
Async Batch Embed Contents
- Async Batch Embed Content
Enqueue large batches of embedding requests for cost-effective asynchronous processing
Files
- Get File
Get metadata about an uploaded file
- List Files
List all uploaded files
- Delete File
Delete an uploaded file
Generate Images
- Generate Image
Generate images using Imagen 4 models (paid account required)
Models
- List Models
List all available Gemini models
- Get Model
Get details about a specific Gemini model
Operation Status
- Get Operation Status
Get status of a long-running operation (batch, video generation, etc.)
Cancel Operations
- Cancel Operation
Cancel a long-running operation
Operations
- List Operations
List all long-running operations
Count Tokens
- Count Tokens
Count tokens in a prompt without generating content
Generate Videos
- Generate Video
Generate videos using Veo models (paid account or quota required)
Set Up Your Google Gemini MCP Server in Minutes
One endpoint. Any framework. Your agent is talking to Google Gemini in under 10 lines of code.
MCP Clients
Agent Frameworks
JSON{
"mcpServers": {
"stackone": {
"command": "npx",
"args": [
"-y",
"mcp-remote@latest",
"https://api.stackone.com/mcp?x-account-id=<account_id>",
"--header",
"Authorization: Basic <YOUR_BASE64_TOKEN>"
]
}
}
}More AI & ML MCP Servers
Google Gemini MCP Server Resources
MCP Code Mode: Keeping Tool Responses Out of Agent Context
Anthropic's code_execution processes data already in context. Custom MCP code mode keeps raw tool responses in a sandbox. 14K tokens vs 500.
11 min
Comparing BM25, TF-IDF, and Hybrid Search for MCP Tool Discovery
Benchmarking BM25, TF-IDF, and hybrid search for MCP tool discovery across 916 tools. The 80/20 TF-IDF/BM25 hybrid hits 21% Top-1 accuracy in under 1ms.
10 min
Indirect Prompt Injection Defense for MCP Tools: A Technical Guide
MCP tools that read emails, CRM records, and tickets are indirect prompt injection vectors. Here's how we built a two-tier defense that scans tool results in ~11ms.
12 min
Google Gemini MCP Server FAQ
Google Gemini MCP server vs direct API integration — what's the difference?
A Google Gemini MCP server and direct API integration serve different use cases. Direct API integration is for software-to-software — backend code calling Google Gemini. A Google Gemini MCP server is for AI agents — MCP clients like Claude and Cursor, plus framework agents built with OpenAI, LangChain, or Vercel AI — discovering and calling Google Gemini at runtime. StackOne provides both.
How does Google Gemini authentication work for AI agents?
Google Gemini authentication for AI agents works through a StackOne Connect Session. Create one via the dashboard or the SDK — you get an auth link and ready-to-paste config for Claude Desktop, Cursor, and other MCP clients. Your user authenticates their own Google Gemini account; StackOne handles token exchange, storage, and refresh. Credentials never reach the LLM, and each user is isolated via
origin_owner_id.Are Google Gemini MCP tools vulnerable to prompt injection?
Yes — Google Gemini MCP tools can be vulnerable to indirect prompt injection. Any tool that reads user-written content — documents, messages, tickets, records, or free-text fields — is a potential vector. StackOne Defender scans every tool response before it enters the agent's context — regex patterns in ~1ms, then a MiniLM classifier in ~4ms. 88.7% accuracy, CPU-only.
What is the context bloat of a Google Gemini agent and how do I avoid it?
Context bloat happens when Google Gemini tool schemas and API responses eat your Google Gemini agent's memory, preventing it from reasoning effectively. A single Google Gemini query can return a massive JSON response, and connecting multiple tools compounds the problem. Tools Discovery and Code Mode reduce context bloat — loading only relevant tools per query and keeping raw responses out of the agent's context.
Can I limit which actions my Google Gemini agent can access?
Yes — you can limit which actions your Google Gemini agent can access directly from the StackOne dashboard. Toggle actions on or off, or restrict them to specific accounts, with no code changes to your agent. Session tokens can be scoped to exact actions so if one leaks, exposure stays contained.
Can I create custom agent actions for my Google Gemini MCP server?
Yes — you can create custom agent actions for your Google Gemini MCP server using Connector Builder. It's an integration agent your coding assistant (Claude Code, Cursor, or Copilot) can invoke to research Google Gemini's API, generate production-ready connector YAML, test against the live API, and validate before you ship.
When should I NOT use a Google Gemini MCP server?
Skip a Google Gemini MCP server if your integration is purely software-to-software — direct Google Gemini API integration is simpler when no AI agent is involved. For deterministic, compliance-critical operations (financial transactions, regulatory reporting), direct API gives you predictable behavior without agent-driven decision-making. MCP shines when AI agents need to dynamically discover and call Google Gemini actions at runtime.
What AI frameworks and AI clients does the StackOne Google Gemini MCP server support?
The StackOne Google Gemini MCP server supports both. MCP clients (paste-and-go apps): Claude Desktop, Claude Code, Cursor, VS Code, Goose. Agent frameworks (code SDKs you build with): OpenAI Agents SDK, Anthropic, Vercel AI, Google ADK, CrewAI, Pydantic AI, LangChain, LangGraph, Azure AI Foundry.
Put your AI agents to work
All the tools you need to build and scale AI agent integrations, with best-in-class connectivity, execution, and security.