MCP Protocol Guide 2026: Connect AI Agents to Any Model

Published: August 26, 2026

What is MCP? Model Context Protocol (MCP) is an open standard that lets AI agents discover and call external tools dynamically — like a USB-C port for AI applications.

Why MCP Matters in 2026

AI agents are becoming the primary interface for software. But agents need to:

Search the web
Query databases
Call APIs
Generate images
Execute code

Without a standard protocol, each integration is bespoke. MCP solves this by providing a universal interface for tool discovery and execution.

How MCP Works

MCP uses Server-Sent Events (SSE) for real-time communication:

┌─────────────┐     SSE Stream      ┌─────────────┐
│  AI Agent   │ ◄─────────────────► │ MCP Server  │
│  (Client)   │   JSON-RPC 2.0      │  (Tools)    │
└─────────────┘                     └─────────────┘
         │                                │
         │  1. tools/list                 │
         │◄───────────────────────────────┤
         │  2. tools/call                 │
         │───────────────────────────────►│
         │  3. Result                     │
         │◄───────────────────────────────┤

TokenEase MCP Server

TokenEase provides an MCP server that exposes Chinese AI models as tools:

Configuration

{
  "mcpServers": {
    "tokenease": {
      "url": "https://tokenease.io/mcp/sse"
    }
  }
}

Available Tools

Tool	Description
`tokenease_chat`	Send chat completion to any supported model
`tokenease_list_models`	List available models with pricing info

Building an MCP Client in Python

import json, requests

# Connect to MCP server
response = requests.get("https://tokenease.io/mcp/sse", stream=True)

# Read initialization message
for line in response.iter_lines():
    if line.startswith(b"data: "):
        data = json.loads(line[6:])
        print(data)
        break

# List available tools
tools_request = {
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/list"
}
response = requests.post("https://tokenease.io/mcp/messages", json=tools_request)
print(response.json())

Supported Models via MCP

DeepSeek V4 (with peak-valley pricing)
Kimi K2.6
GLM-4-Flash
Qwen-Max
Doubao-Pro
Hunyuan
Spark

Try TokenEase MCP

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TokenEase MCP server implements MCP protocol version 2024-11-05 with SSE transport.