Last month, our e-commerce platform faced a critical challenge: we needed to launch an AI customer service system that could handle 10,000 concurrent requests during our flash sale event while integrating with legacy inventory management tools. Our existing infrastructure relied on OpenAI Function Calling, but our new AI vendor supported only the Model Context Protocol (MCP). Rebuilding everything from scratch would have taken 6 weeks we didn't have. I spent three intense days building an adapter layer that transparently bridges these two ecosystems—and I'm going to walk you through exactly how I did it.
By the end of this tutorial, you'll have a production-ready adapter that converts MCP tool definitions to OpenAI Function Calling format and vice versa, with proper request/response transformation. I built this using HolySheep AI as our backend—where API calls cost just $1 per dollar equivalent (saving 85%+ compared to ¥7.3 rates elsewhere), support WeChat and Alipay payments, deliver under 50ms latency, and include free credits on signup.
The Problem: Two Incompatible Tool-Calling Standards
The AI ecosystem has fragmented into two dominant tool-calling paradigms. OpenAI's Function Calling, introduced in mid-2023, uses a structured JSON schema for tool definitions:
{
"type": "function",
"function": {
"name": "check_inventory",
"description": "Check product stock levels",
"parameters": {
"type": "object",
"properties": {
"product_id": {"type": "string"},
"warehouse_id": {"type": "string", "optional": true}
},
"required": ["product_id"]
}
}
}MCP, developed by Anthropic, uses a different schema with tool annotations, resource URIs, and prompt templates:
{
"tool": "check_inventory",
"inputSchema": {
"type": "object",
"properties": {
"product_id": {"type": "string"},
"warehouse_id": {"type": "string"}
},
"required": ["product_id"]
},
"description": "Check product stock levels",
"annotations": {
"readOnlyHint": true,
"destructiveHint": false
}
}These formats are similar but incompatible. When you need to support both protocols simultaneously—or migrate from one to the other—you need a proper translation layer.
Architecture Overview
Our adapter layer performs bidirectional translation with three core components:
- MCP to OpenAI Converter: Transforms MCP tool definitions into OpenAI Function format
- OpenAI to MCP Converter: Transforms OpenAI function definitions into MCP format
- Request Router: Dispatches calls to the appropriate backend based on protocol detection
Implementation: Building the Adapter Layer
I implemented this adapter in Python using FastAPI for the web layer and a clean class hierarchy for protocol conversion. Here's the complete solution:
# mcp_openai_adapter.py
import json
import re
from typing import Any, Dict, List, Optional, Union
from dataclasses import dataclass, field
from enum import Enum
class ProtocolType(Enum):
MCP = "mcp"
OPENAI = "openai"
@dataclass
class ToolParameter:
name: str
type: str
description: str = ""
required: bool = False
default: Any = None
enum_values: List[Any] = field(default_factory=list)
@dataclass
class ToolDefinition:
name: str
description: str
parameters: List[ToolParameter]
annotations: Dict[str, Any] = field(default_factory=dict)
class MCPToOpenAIConverter:
"""Converts MCP tool definitions to OpenAI Function Calling format."""
TYPE_MAPPING = {
"string": "string",
"integer": "integer",
"number": "number",
"boolean": "boolean",
"array": "array",
"object": "object"
}
def convert_tool(self, mcp_tool: Dict[str, Any]) -> Dict[str, Any]:
"""Transform a single MCP tool definition to OpenAI function format."""
name = mcp_tool.get("tool", mcp_tool.get("name", ""))
description = mcp_tool.get("description", "")
input_schema = mcp_tool.get("inputSchema", mcp_tool.get("parameters", {}))
# Handle both "inputSchema" and "parameters" field names
if "parameters" in mcp_tool and "inputSchema" not in mcp_tool:
input_schema = mcp_tool["parameters"]
properties = {}
required = []
if isinstance(input_schema, dict):
schema_properties = input_schema.get("properties", {})
schema_required = input_schema.get("required", [])
for param_name, param_def in schema_properties.items():
param_type = self.TYPE_MAPPING.get(
param_def.get("type", "string"),
"string"
)
param_obj = {
"type": param_type,
"description": param_def.get("description", "")
}
# Handle enums
if "enum" in param_def:
param_obj["enum"] = param_def["enum"]
# Handle defaults
if "default" in param_def:
param_obj["default"] = param_def["default"]
properties[param_name] = param_obj
if param_name in schema_required:
required.append(param_name)
return {
"type": "function",
"function": {
"name": name,
"description": description,
"parameters": {
"type": "object",
"properties": properties,
"required": required
}
}
}
def convert_tools(self, mcp_tools: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""Convert multiple MCP tools to OpenAI function format."""
return [self.convert_tool(tool) for tool in mcp_tools]
class OpenAIToMCPConverter:
"""Converts OpenAI Function Calling definitions to MCP format."""
def convert_function(self, openai_function: Dict[str, Any]) -> Dict[str, Any]:
"""Transform a single OpenAI function definition to MCP format."""
func_def = openai_function.get("function", openai_function)
name = func_def.get("name", "")
description = func_def.get("description", "")
parameters = func_def.get("parameters", {})
# Extract properties and required fields
properties = parameters.get("properties", {})
required = parameters.get("required", [])
# Build MCP-style input schema
input_schema = {
"type": "object",
"properties": {},
"required": required
}
for param_name, param_def in properties.items():
input_schema["properties"][param_name] = {
"type": param_def.get("type", "string"),
"description": param_def.get("description", "")
}
# Preserve enums
if "enum" in param_def:
input_schema["properties"][param_name]["enum"] = param_def["enum"]
# Preserve defaults
if "default" in param_def:
input_schema["properties"][param_name]["default"] = param_def["default"]
mcp_tool = {
"tool": name,
"description": description,
"inputSchema": input_schema
}
# Add annotations if available
if "annotations" in openai_function:
mcp_tool["annotations"] = openai_function["annotations"]
return mcp_tool
def convert_functions(self, openai_functions: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""Convert multiple OpenAI functions to MCP format."""
return [self.convert_function(func) for func in openai_functions]
class ToolAdapter:
"""
Main adapter class that handles bidirectional protocol translation.
Supports HolySheep AI as the backend provider.
"""
def __init__(self, api_key: str, base_url: str = "https://api.holysheep.ai/v1"):
self.api_key = api_key
self.base_url = base_url
self.mcp_converter = MCPToOpenAIConverter()
self.openai_converter = OpenAIToMCPConverter()
def detect_protocol(self, tool_definition: Dict[str, Any]) -> ProtocolType:
"""Auto-detect whether a tool definition is MCP or OpenAI format."""
# MCP tools have "tool" field or "inputSchema" field
if "tool" in tool_definition or "inputSchema" in tool_definition:
return ProtocolType.MCP
# OpenAI tools have "type" and nested "function" field
if tool_definition.get("type") == "function" and "function" in tool_definition:
return ProtocolType.OPENAI
# Default to OpenAI for backward compatibility
return ProtocolType.OPENAI
def normalize_to_openai(self, tools: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""Convert any protocol tool definitions to OpenAI format."""
normalized = []
for tool in tools:
protocol = self.detect_protocol(tool)
if protocol == ProtocolType.MCP:
normalized.append(self.mcp_converter.convert_tool(tool))
else:
normalized.append(tool)
return normalized
def normalize_to_mcp(self, tools: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""Convert any protocol tool definitions to MCP format."""
normalized = []
for tool in tools:
protocol = self.detect_protocol(tool)
if protocol == ProtocolType.OPENAI:
normalized.append(self.openai_converter.convert_function(tool))
else:
normalized.append(tool)
return normalized
def translate_response(
self,
response: Dict[str, Any],
target_protocol: ProtocolType
) -> Dict[str, Any]:
"""Translate a function call response to the target protocol format."""
if target_protocol == ProtocolType.MCP:
# Convert OpenAI-style tool calls to MCP format
if "tool_calls" in response:
mcp_calls = []
for call in response["tool_calls"]:
mcp_calls.append({
"tool": call["function"]["name"],
"arguments": json.loads(call["function"]["arguments"])
})
return {"tool_calls": mcp_calls}
elif target_protocol == ProtocolType.OPENAI:
# Convert MCP-style tool calls to OpenAI format
if "tool_calls" in response:
openai_calls = []
for call in response["tool_calls"]:
openai_calls.append({
"id": f"call_{hash(call['tool'])}",
"type": "function",
"function": {
"name": call["tool"],
"arguments": json.dumps(call["arguments"])
}
})
return {"tool_calls": openai_calls}
return response
Example usage
if __name__ == "__main__":
adapter = ToolAdapter(api_key="YOUR_HOLYSHEEP_API_KEY")
# Sample MCP tool definition
mcp_tool = {
"tool": "get_order_status",
"description": "Retrieve the current status of a customer order",
"inputSchema": {
"type": "object",
"properties": {
"order_id": {"type": "string", "description": "Unique order identifier"},
"include_timeline": {"type": "boolean", "description": "Include event timeline"}
},
"required": ["order_id"]
},
"annotations": {"readOnlyHint": True}
}
# Convert to OpenAI format
openai_func = adapter.normalize_to_openai([mcp_tool])[0]
print("Converted to OpenAI:")
print(json.dumps(openai_func, indent=2))Building the API Service Layer
Now I'll create a FastAPI service that exposes both protocols through a unified interface. This service integrates with HolySheep AI, which provides $1 per dollar equivalent pricing (85%+ savings versus ¥7.3 rates), WeChat and Alipay payment support, sub-50ms latency, and free credits upon registration:
# app.py
import os
import json
import httpx
from typing import List, Dict, Any, Optional
from fastapi import FastAPI, HTTPException, Header, Request
from fastapi.responses import JSONResponse
from pydantic import BaseModel, Field
from mcp_openai_adapter import ToolAdapter, ProtocolType
app = FastAPI(title="MCP-OpenAI Adapter Service", version="1.0.0")
Configuration
HOLYSHEEP_API_KEY = os.getenv("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
Initialize adapter
adapter = ToolAdapter(api_key=HOLYSHEEP_API_KEY, base_url=HOLYSHEEP_BASE_URL)
class ChatMessage(BaseModel):
role: str = "user"
content: str
class ToolCall(BaseModel):
id: str
type: str = "function"
function: Dict[str, Any]
class ChatCompletionRequest(BaseModel):
model: str = "gpt-4o"
messages: List[ChatMessage]
tools: Optional[List[Dict[str, Any]]] = None
tool_choice: Optional[str] = "auto"
temperature: float = 0.7
max_tokens: int = 2000
class MCPToolCallRequest(BaseModel):
tool_name: str
arguments: Dict[str, Any]
class MCPRequest(BaseModel):
messages: List[ChatMessage]
tools: List[Dict[str, Any]]
model: str = "claude-sonnet-4.5"
max_tokens: int = 2000
temperature: float = 0.7
@app.post("/v1/chat/completions")
async def openai_chat_completions(
request: ChatCompletionRequest,
authorization: str = Header(..., alias="Authorization")
):
"""
OpenAI-compatible chat completions endpoint.
Automatically translates MCP tools to OpenAI format if needed.
"""
# Extract bearer token
token = authorization.replace("Bearer ", "")
# Normalize tools to OpenAI format if they come as MCP
if request.tools:
normalized_tools = adapter.normalize_to_openai(request.tools)
else:
normalized_tools = None
# Prepare payload for HolySheep AI
payload = {
"model": request.model,
"messages": [{"role": m.role, "content": m.content} for m in request.messages],
"temperature": request.temperature,
"max_tokens": request.max_tokens,
}
if normalized_tools:
payload["tools"] = normalized_tools
payload["tool_choice"] = request.tool_choice
# Call HolySheep AI
async with httpx.AsyncClient(timeout=60.0) as client:
response = await client.post(
f"{HOLYSHEEP_BASE_URL}/chat/completions",
headers={
"Authorization": f"Bearer {token}",
"Content-Type": "application/json"
},
json=payload
)
if response.status_code != 200:
raise HTTPException(
status_code=response.status_code,
detail=f"HolySheep AI error: {response.text}"
)
return response.json()
@app.post("/mcp/chat")
async def mcp_chat(request: MCPRequest):
"""
MCP-compatible chat endpoint.
Translates OpenAI-style functions to MCP format.
"""
# Normalize tools to MCP format if they come as OpenAI
normalized_tools = adapter.normalize_to_mcp(request.tools)
# Convert messages to OpenAI format for API call
openai_messages = [{"role": m.role, "content": m.content} for m in request.messages]
# Convert MCP tools to OpenAI format for API call
openai_tools = adapter.normalize_to_openai(request.tools)
# Prepare payload for HolySheep AI
payload = {
"model": request.model,
"messages": openai_messages,
"tools": openai_tools,
"temperature": request.temperature,
"max_tokens": request.max_tokens
}
# Call HolySheep AI
async with httpx.AsyncClient(timeout=60.0) as client:
response = await client.post(
f"{HOLYSHEEP_BASE_URL}/chat/completions",
headers={
"Authorization": f"Bearer {HOLYSHEEP_API_KEY}",
"Content-Type": "application/json"
},
json=payload
)
if response.status_code != 200:
raise HTTPException(
status_code=response.status_code,
detail=f"HolySheep AI error: {response.text}"
)
# Convert response back to MCP format
openai_response = response.json()
mcp_response = adapter.translate_response(openai_response, ProtocolType.MCP)
return {
"model": request.model,
"messages": openai_response.get("choices", [{}])[0].get("message", {}),
"tool_calls": mcp_response.get("tool_calls", []),
"usage": openai_response.get("usage", {})
}
@app.post("/mcp/execute")
async def execute_mcp_tool(
tool_name: str,
arguments: Dict[str, Any]
):
"""
Execute a single MCP tool with the given arguments.
Replace this with your actual tool implementation.
"""
# Tool registry - replace with your actual implementations
tool_registry = {
"check_inventory": check_inventory,
"get_order_status": get_order_status,
"calculate_shipping": calculate_shipping
}
if tool_name not in tool_registry:
raise HTTPException(status_code=404, detail=f"Tool '{tool_name}' not found")
try:
result = await tool_registry[tool_name](**arguments)
return {"result": result, "tool": tool_name, "success": True}
except Exception as e:
return {"error": str(e), "tool": tool_name, "success": False}
Sample tool implementations
async def check_inventory(product_id: str, warehouse_id: str = "default") -> Dict[str, Any]:
"""Sample inventory check implementation."""
return {
"product_id": product_id,
"warehouse_id": warehouse_id,
"quantity": 150,
"available": True,
"restock_date": "2026-01-15"
}
async def get_order_status(order_id: str, include_timeline: bool = False) -> Dict[str, Any]:
"""Sample order status implementation."""
status = {
"order_id": order_id,
"status": "shipped",
"estimated_delivery": "2026-01-12",
"tracking_number": "TRK123456789"
}
if include_timeline:
status["timeline"] = [
{"event": "Order placed", "timestamp": "2026-01-08T10:30:00Z"},
{"event": "Payment confirmed", "timestamp": "2026-01-08T10:31:00Z"},
{"event": "Shipped", "timestamp": "2026-01-09T14:00:00Z"}
]
return status
async def calculate_shipping(
destination: str,
weight: float,
shipping_method: str = "standard"
) -> Dict[str, Any]:
"""Sample shipping calculation implementation."""
rates = {
"standard": 5.99,
"express": 12.99,
"overnight": 29.99
}
base_rate = rates.get(shipping_method, rates["standard"])
total = base_rate + (weight * 0.50)
return {
"destination": destination,
"weight": weight,
"method": shipping_method,
"base_rate": base_rate,
"weight_charge": weight * 0.50,
"total": round(total, 2),
"currency": "USD"
}
@app.get("/health")
async def health_check():
"""Health check endpoint for monitoring."""
return {
"status": "healthy",
"adapter_version": "1.0.0",
"supported_protocols": ["openai", "mcp"],
"backend": "HolySheep AI"
}
Run with: uvicorn app:app --host 0.0.0.0 --port 8000
Client-Side Integration Examples
Here's how to integrate this adapter from different client perspectives:
# client_examples.py
import requests
import json
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"
ADAPTER_URL = "http://localhost:8000"
def test_openai_compatible():
"""Test using OpenAI SDK-compatible endpoint."""
# Define tools in OpenAI format
tools = [
{
"type": "function",
"function": {
"name": "check_inventory",
"description": "Check product stock levels in warehouse",
"parameters": {
"type": "object",
"properties": {
"product_id": {"type": "string", "description": "Product SKU"},
"warehouse_id": {"type": "string", "description": "Warehouse code"}
},
"required": ["product_id"]
}
}
}
]
payload = {
"model": "gpt-4o",
"messages": [
{"role": "user", "content": "Do we have SKU-12345 in warehouse WH-EAST?"}
],
"tools": tools,
"tool_choice": "auto"
}
response = requests.post(
f"{ADAPTER