How to Use the Easemob / 环信 MCP in Google ADK

Q: Can I use Google ADK to query Easemob / 环信 data and store it in BigQuery?

Yes, your Google ADK agent can call getchathistory to extract message logs and then pipe that structured data directly into BigQuery for long-term storage and machine learning analysis.

Q: How do I configure the transport layer for Easemob / 环信 in Google ADK?

You initialize the connection using the McpToolset class with the Streamable HTTP transport parameters. The SDK automatically discovers the tools like sendtextmessage and exposes them to your Gemini-powered agent.

Q: How does Gemini handle the JSON payloads returned by the chat API?

The model parses the JSON returned by endpoints like getgroup natively. Gemini's strong reasoning capabilities make it easy to extract specific fields like member lists or creation timestamps without extra parsing code.

Q: How is my chat history secured during Google ADK operations?

All chat history and user details retrieved by getchathistory are processed in transit via secure HTTPS. The Vinkius MCP Server executes the calls ephemerally, ensuring no customer chat logs are persisted outside your Google Cloud environment.

Connect Gemini's long-context reasoning with Easemob / 环信 using Google ADK to run enterprise chat operations.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Easemob / 环信 MCP to Google ADK

Create your Vinkius account to connect Easemob / 环信 to Google ADK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Easemob / 环信 with Google ADK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Analyze thousands of messages using Google ADK

Feed entire conversation histories directly into Gemini's massive context window via Google ADK. Your Google ADK agent runs `get_chat_history` to pull deep Easemob / 环信 message logs and feeds them to the LLM to identify support trends. Because Google ADK integrates with Vertex AI, you can run complex analytical prompts over thousands of Easemob / 环信 messages without hitting token limits. Your Google ADK agent then writes its summary back to the team using `send_text_message`.

Keep your database in sync with this MCP Server

The MCP Server links your BigQuery tables directly to your Easemob / 环信 chat infrastructure. Your Google ADK agent calls `list_users` to see who is currently registered in your Easemob / 环信 chat system. If it finds a mismatch with your cloud database, the Google ADK agent runs `register_user` to create the missing profiles. You can also keep tabs on individual Easemob / 环信 account details using `get_user` with Google ADK.

Automated Customer Support Rooms

When a high-priority ticket comes into your Google ADK system, the agent creates a dedicated Easemob / 环信 triage channel using `create_group`. It immediately adds the assigned engineers and customer accounts to Easemob / 环信 by calling `add_group_member`. Your Google ADK agent then inspects the group details with `get_group` to verify everyone was added. This automation cuts out the manual overhead of setting up Easemob / 环信 channels for urgent client issues.

Setup guide

Set up Easemob / 环信 MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Easemob / 环信 tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Easemob / 环信_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Easemob / 环信 tools via MCP.",
    tools=mcp_tools,
)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Easemob / 环信. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Easemob / 环信 now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Easemob / 环信 MCP in Google ADK

Yes, your Google ADK agent can call `get_chat_history` to extract message logs and then pipe that structured data directly into BigQuery for long-term storage and machine learning analysis.

You initialize the connection using the `McpToolset` class with the Streamable HTTP transport parameters. The SDK automatically discovers the tools like `send_text_message` and exposes them to your Gemini-powered agent.

Yes, the MCP Server supports an optional tool names filter. You can restrict the agent's access to read-only operations like `get_user` and `list_groups` if you want to prevent accidental modifications.

The model parses the JSON returned by endpoints like `get_group` natively. Gemini's strong reasoning capabilities make it easy to extract specific fields like member lists or creation timestamps without extra parsing code.

All chat history and user details retrieved by `get_chat_history` are processed in transit via secure HTTPS. The Vinkius MCP Server executes the calls ephemerally, ensuring no customer chat logs are persisted outside your Google Cloud environment.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript