How do I find my ZEGO AppID and ServerSecret?

Log in to the [ZEGO Admin Console](https://console.zego.im/), create or select a project, and you will find your AppID and ServerSecret in the project configuration or basic information section.

What is the SignatureNonce used for?

The SignatureNonce is a random string used to prevent replay attacks. This server automatically generates a fresh nonce for every request to ensure high security for your RTC management.

Can I kick a user from a specific room?

Yes! Use the `kick_room_user` tool with the target RoomId and UserId. You can also provide a custom message that the user will see when they are disconnected.

ZEGO / 即构科技 MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: ZEGO / 即构科技
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect ZEGO / 即构科技 through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to ZEGO / 即构科技 "
            "(8 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in ZEGO / 即构科技?"
    )
    print(result.data)

asyncio.run(main())

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High SecurityEnterprise-grade

IAMAccess control

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DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About ZEGO / 即构科技 MCP Server

Empower your AI agent to orchestrate your real-time communication infrastructure with ZEGO (即构科技), the premier provider of global video and audio RTC services. By connecting ZEGO to your agent, you transform complex room management, stream control, and user status tracking into a natural conversation. Your agent can instantly retrieve active room lists, monitor user counts, force-stop media streams, and audit service usage statistics without you ever needing to navigate multiple technical dashboards. Whether you are building an automated moderation system for live rooms or monitoring cross-regional connectivity, your agent acts as a real-time RTC operations assistant, providing accurate and reliable results from a single, authorized source.

Pydantic AI validates every ZEGO / 即构科技 tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Room Orchestration — List active rooms, retrieve detailed metadata, and monitor real-time user activity.
User Management — Track user status (online/offline), list members in specific rooms, and manage access (kick users).
Stream Control — Monitor active media streams and force-terminate unauthorized or problematic broadcasts.
Usage Auditing — Retrieve comprehensive audio and video duration statistics for specific time ranges.
Operational Insights — Monitor total online user counts and API connectivity status to ensure system-wide health.

The ZEGO / 即构科技 MCP Server exposes 8 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect ZEGO / 即构科技 to Pydantic AI via MCP

Follow these steps to integrate the ZEGO / 即构科技 MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 8 tools from ZEGO / 即构科技 with type-safe schemas

Why Use Pydantic AI with the ZEGO / 即构科技 MCP Server

Pydantic AI provides unique advantages when paired with ZEGO / 即构科技 through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your ZEGO / 即构科技 integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your ZEGO / 即构科技 connection logic from agent behavior for testable, maintainable code

ZEGO / 即构科技 + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the ZEGO / 即构科技 MCP Server delivers measurable value.

Type-safe data pipelines: query ZEGO / 即构科技 with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple ZEGO / 即构科技 tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query ZEGO / 即构科技 and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock ZEGO / 即构科技 responses and write comprehensive agent tests

ZEGO / 即构科技 MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect ZEGO / 即构科技 to Pydantic AI via MCP:

check_user_status

Check status of multiple users

get_online_count

Get total online user count

get_room_streams

List active streams in a room

get_room_users

List users in a room

get_usage_stats

Get service usage statistics

kick_room_user

Kick user from room

list_rooms

List active rooms

stop_media_stream

Force stop a stream

Example Prompts for ZEGO / 即构科技 in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with ZEGO / 即构科技 immediately.

"List all active rooms in our ZEGO project."

"Check the status for these users: 'user_01,user_02'."

"What is our video usage duration for the last 7 days?"

Troubleshooting ZEGO / 即构科技 MCP Server with Pydantic AI

Common issues when connecting ZEGO / 即构科技 to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

ZEGO / 即构科技 + Pydantic AI FAQ

Common questions about integrating ZEGO / 即构科技 MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your ZEGO / 即构科技 MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.