Connect
Optimize
Secure
Google Chat MCP Server
for AI Agents
Production-ready Google Chat MCP server with 30 extensible actions — plus built-in authentication, security, and optimized execution.
Google Chat MCP Server
Built by 
StackOne
StackOne 
Coverage
30 Agent Actions
Create, read, update, and delete across Google Chat — and extend your agent's capabilities with custom actions.
Authentication
Agent Tool Authentication
Per-user OAuth in one call. Your Google Chat MCP server gets session-scoped tokens with zero credentials stored on your infra.
Agent Auth →
Security
Agent Protection
Every Google Chat 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 Chat call.
Tools Discovery →What is the Google Chat MCP Server?
A Google Chat MCP server lets AI agents read and write Google Chat data through the Model Context Protocol — Anthropic's open standard for connecting LLMs to external tools. StackOne's Google Chat MCP server ships with 30 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 Chat MCP Tools and Actions
Every action from Google Chat's API, ready for your agent. Create, read, update, and delete — scoped to exactly what you need.
Spaces
- Create Space
Create a new space
- List Spaces
List spaces where the caller is a member
- Get Space
Get a single space by resource name
- Update Space
Update an existing space
- Delete Space
Delete a space
Find Direct Messages
- Find Direct Message
Find existing direct message with a specified user
Setup Spaces
- Setup Space
Create a space and add specified users
Members
- Create Member
Create a membership for a user or Chat app
- List Members
List memberships in a space
- Get Member
Get a single membership by resource name
- Update Member
Update a membership
- Delete Member
Delete a membership
Messages
- Create Message
Create a message in a space
- List Messages
List messages in a space
- Get Message
Get a single message by resource name
- Update Message
Update an existing message
- Delete Message
Delete a message
Patch Messages
- Patch Message
Partially update a message
Reactions
- Create Reaction
Add a reaction to a message
- List Reactions
List reactions for a message
- Delete Reaction
Delete a message reaction
Attachments
- Get Attachment
Get message attachment metadata
Custom Emojis
- List Custom Emojis
List custom emojis visible to the authenticated user
- Get Custom Emoji
Get custom emoji details
Space Read States
- Get Space Read State
Get user's read state within a space
- Update Space Read State
Update user's read state within a space
Thread Read States
- Get Thread Read State
Get user's read state within a thread
Space Notification Settings
- Get Space Notification Setting
Get space notification settings
- Update Space Notification Setting
Update space notification settings
Current Users
- Get Current User
Get authenticated user information
Set Up Your Google Chat MCP Server in Minutes
One endpoint. Any framework. Your agent is talking to Google Chat 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>"
]
}
}
}Google Chat 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 Chat MCP Server FAQ
Google Chat MCP server vs direct API integration — what's the difference?
A Google Chat MCP server and direct API integration serve different use cases. Direct API integration is for software-to-software — backend code calling Google Chat. A Google Chat 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 Chat at runtime. StackOne provides both.
How does Google Chat authentication work for AI agents?
Google Chat 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 Chat account; StackOne handles token exchange, storage, and refresh. Credentials never reach the LLM, and each user is isolated via
origin_owner_id.Are Google Chat MCP tools vulnerable to prompt injection?
Yes — Google Chat 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 Chat agent and how do I avoid it?
Context bloat happens when Google Chat tool schemas and API responses eat your Google Chat agent's memory, preventing it from reasoning effectively. A single Google Chat 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 Chat agent can access?
Yes — you can limit which actions your Google Chat 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 Chat MCP server?
Yes — you can create custom agent actions for your Google Chat MCP server using Connector Builder. It's an integration agent your coding assistant (Claude Code, Cursor, or Copilot) can invoke to research Google Chat's API, generate production-ready connector YAML, test against the live API, and validate before you ship.
When should I NOT use a Google Chat MCP server?
Skip a Google Chat MCP server if your integration is purely software-to-software — direct Google Chat 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 Chat actions at runtime.
What AI frameworks and AI clients does the StackOne Google Chat MCP server support?
The StackOne Google Chat 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.