MCP Calling

MCP server calling is based on the Model Context Protocol (MCP) standard, dynamically connecting and calling tools and resources provided by external MCP servers in chat/completions. Through MCP server calling, you can extend the model’s capability boundaries, implementing various functions such as search, visual understanding, file processing, data analysis, and more, providing powerful tool ecosystem support for AI applications.

Features

The MCP server calling feature integrates MCP tool calling capabilities in chat, supporting direct calls to chat/completions to connect and use various MCP servers to extend model functionality without requiring an MCP client. Through MCP server calling, AI can:

Dynamic Tool Discovery: Automatically discover and enumerate tools provided by MCP servers
Real-time Tool Calling: Call various tools and functions from external MCP servers
Multi-protocol Support: Support SSE and Streamable-HTTP transport protocols
Flexible Configuration: Support custom server addresses, authentication information, and tool permissions
Zhipu Ecosystem: Built-in official Zhipu MCP servers, direct use without URL configuration
Standard Compatibility: Fully compatible with MCP protocol standards, supporting third-party MCP servers

Supported Transport Protocols

SSE (Server-Sent Events)

Use Cases: Real-time data streams, long connection scenarios
Features: Unidirectional data flow, server-initiated push
Simple Configuration: Pass authentication information through URL parameters

Streamable-HTTP

Use Cases: Standard HTTP request-response
Features: Bidirectional communication, supports complex interactions
Flexible Configuration: Support custom Headers and parameters

Parameter Description

server_label

string

required

MCP server identifier. If connecting to official Zhipu MCP servers, use mcp code to fill this field, no need to fill server_url

server_url

string

MCP server address. Required when connecting to third-party MCP servers

transport_type

string

default:"streamable-http"

Transport protocol type

sse: Server-Sent Events protocol
streamable-http: Standard HTTP protocol (default)

allowed_tools

array

Collection of allowed tools to call. If not specified, all available tools are allowed

["webSearchPrime", "image_analysis", "video_analysis"]

headers

object

Authentication information and custom headers required by MCP server

{
  "Authorization": "Bearer your_api_key",
  "Custom-Header": "custom_value"
}

Code Examples

cURL
Python

Connect to Official Zhipu MCP Server

curl --location 'https://api.z.ai/api/paas/v4/chat/completions' \
--header 'Authorization: Bearer YOUR_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
    "model": "glm-4.6",
    "messages": [
        {
            "role": "user",
            "content": "Search for the latest artificial intelligence development trends"
        }
    ],
    "tools": [
        {
            "type": "mcp",
            "mcp": {
                "server_label": "mcp code",
                "transport_type": "streamable-http",
                "allowed_tools": ["webSearchPrime"]
            }
        }
    ],
    "tool_choice": "auto"
}'

Connect to Third-party MCP Server

curl --location 'https://api.z.ai/api/paas/v4/chat/completions' \
--header 'Authorization: Bearer YOUR_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
    "model": "glm-4.6",
    "messages": [
        {
            "role": "user",
            "content": "Analyze the content of this document"
        }
    ],
    "tools": [
        {
            "type": "mcp",
            "mcp": {
                "server_label": "document-analyzer",
                "server_url": "https://doc-mcp.example.com/mcp",
                "transport_type": "streamable-http",
                "headers": {
                    "Authorization": "Bearer doc_analysis_token",
                    "X-Custom-Header": "custom_value"
                },
                "allowed_tools": ["analyze_document", "extract_text"]
            }
        }
    ],
    "tool_choice": "auto"
}'

Using SSE Protocol

curl --location 'https://api.z.ai/api/paas/v4/chat/completions' \
--header 'Authorization: Bearer YOUR_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
    "model": "glm-4.6",
    "messages": [
        {
            "role": "user",
            "content": "Get real-time weather information"
        }
    ],
    "tools": [
        {
            "type": "mcp",
            "mcp": {
                "server_label": "weather-service",
                "server_url": "https://weather-mcp.example.com/sse?token=weather_token",
                "transport_type": "sse",
                "allowed_tools": ["get_current_weather", "get_forecast"]
            }
        }
    ],
    "tool_choice": "auto"
}'

Install SDK

# Install latest version
pip install zai-sdk

# Or specify version
pip install zai-sdk==0.1.0

Verify Installation

import zai
print(zai.__version__)

Connect to Official Zhipu MCP Server

from zai import ZaiClient

# Initialize client
client = ZaiClient(api_key='Your API Key')

# Use official Zhipu MCP server (no need to configure server_url)
response = client.chat.completions.create(
    model="glm-4.6",
    messages=[
        {
            "role": "user",
            "content": "Help me search for the latest AI technology development trends"
        }
    ],
    tools=[
        {
            "type": "mcp",
            "mcp": {
                "server_label": "mcp code",
                "transport_type": "streamable-http",
                "allowed_tools": ["webSearchPrime"]  # Only allow search tools
            }
        }
    ],
    tool_choice="auto"
)

print("Search Results:")
print(response.choices[0].message.content)

Connect to Third-party MCP Server

from zai import ZaiClient

# Initialize client
client = ZaiClient(api_key='Your API Key')

# Connect to third-party MCP server
response = client.chat.completions.create(
    model="glm-4.6",
    messages=[
        {
            "role": "user",
            "content": "Analyze the content of this image"
        }
    ],
    tools=[
        {
            "type": "mcp",
            "mcp": {
                "server_label": "vision-server",
                "server_url": "https://your-mcp-server.com/mcp",
                "transport_type": "streamable-http",
                "headers": {
                    "Authorization": "Bearer your_mcp_token",
                    "Content-Type": "application/json"
                },
                "allowed_tools": ["image_analysis", "video_analysis"]
            }
        }
    ],
    tool_choice="auto"
)

print("Analysis Results:")
print(response.choices[0].message.content)

Connect Using SSE Protocol

from zai import ZaiClient

# Initialize client
client = ZaiClient(api_key='Your API Key')

# Connect to MCP server using SSE protocol
response = client.chat.completions.create(
    model="glm-4.6",
    messages=[
        {
            "role": "user",
            "content": "Get real-time stock price information"
        }
    ],
    tools=[
        {
            "type": "mcp",
            "mcp": {
                "server_label": "finance-server",
                "server_url": "https://finance-mcp.example.com/sse?Authorization=your_token",
                "transport_type": "sse",
                "allowed_tools": ["get_stock_price", "get_market_data"]
            }
        }
    ],
    tool_choice="auto"
)

print("Stock Price Information:")
print(response.choices[0].message.content)

Application Scenarios

Intelligent Search

Real-time information retrieval
Web resource search
News updates acquisition

Visual Analysis

Image content recognition
Video analysis processing
Document image parsing

Data Processing

File format conversion
Data cleaning and organization
Report generation and analysis

API Integration

Third-party service calls
Enterprise system integration
Cloud service connections

Get Started

Language Models

Vision Language Models

Image Generation Models

Video Generation Models

Image Generation Models

Audio Models

Capabilities

Tools

Agents

Features

Supported Transport Protocols

SSE (Server-Sent Events)

Streamable-HTTP

Parameter Description

Code Examples

Application Scenarios

Intelligent Search

Visual Analysis

Data Processing

API Integration

Get Started

Language Models

Vision Language Models

Image Generation Models

Video Generation Models

Image Generation Models

Audio Models

Capabilities

Tools

Agents

​Features

​Supported Transport Protocols

​SSE (Server-Sent Events)

​Streamable-HTTP

​Parameter Description

​Code Examples

​Application Scenarios

Intelligent Search

Visual Analysis

Data Processing

API Integration

Features

Supported Transport Protocols

SSE (Server-Sent Events)

Streamable-HTTP

Parameter Description

Code Examples

Application Scenarios