LLM Integration

MCPHub provides OpenRouter-compatible LLM endpoints, allowing you to use your favorite AI models with the same familiar API interface you already know and love.

Quick Start

🚀 Get Started in Minutes

MCPHub's LLM API is fully compatible with OpenRouter's interface. If you've used OpenRouter before, you can switch to MCPHub with just a simple endpoint change!

MCPHub Endpoint

https://api.mcphub.com/v1

OpenRouter Compatible

Same API interface, same parameters, same response format

Using the OpenAI SDK

The easiest way to get started is using the official OpenAI SDK, just like with OpenRouter. Simply point it to MCPHub's endpoint:

Python

from openai import OpenAI

# Initialize the client with MCPHub endpoint
client = OpenAI(
    base_url="https://api.mcphub.com/v1",
    api_key="<YOUR_MCPHUB_API_KEY>",
)

# Make a chat completion request
completion = client.chat.completions.create(
    model="gpt-5-nano",
    messages=[
        {
            "role": "system", 
            "content": "You are a helpful assistant."
        },
        {
            "role": "user", 
            "content": "Hello! What model are you?"
        }
    ]
)

print(completion.choices[0].message.content)

TypeScript/JavaScript

import OpenAI from 'openai';

// Initialize the client with MCPHub endpoint
const client = new OpenAI({
  baseURL: 'https://api.mcphub.com/v1',
  apiKey: '<YOUR_MCPHUB_API_KEY>',
});

// Make a chat completion request
const completion = await client.chat.completions.create({
  model: 'gpt-5-nano',
  messages: [
    {
      role: 'system',
      content: 'You are a helpful assistant.'
    },
    {
      role: 'user',
      content: 'Hello! What model are you?'
    }
  ]
});

console.log(completion.choices[0].message.content);

Using the API Directly

You can also make direct HTTP requests to the MCPHub API using any HTTP client:

cURL

curl -X POST "https://api.mcphub.com/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "X-API-Key: <YOUR_MCPHUB_API_KEY>" \
  -d '{
    "model": "gpt-5-nano",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user", 
        "content": "Hello! What model are you?"
      }
    ]
  }'

Python (requests)

import requests
import json

response = requests.post(
    url="https://api.mcphub.com/v1/chat/completions",
    headers={
        "X-API-Key": "<YOUR_MCPHUB_API_KEY>",
        "Content-Type": "application/json"
    },
    data=json.dumps({
        "model": "gpt-5-nano",
        "messages": [
            {
                "role": "system",
                "content": "You are a helpful assistant."
            },
            {
                "role": "user",
                "content": "Hello! What model are you?"
            }
        ]
    })
)

result = response.json()
print(result["choices"][0]["message"]["content"])

Available Models

MCPHub supports a wide range of popular LLM models. Here are some examples:

OpenAI Models

  • gpt-5-nano
  • openai/gpt-4o
  • openai/gpt-4o-mini

Other Popular Models

  • claude-3-5-sonnet-20240620
  • gemini-2.0-flash
  • deepseek-chat

📋 Get Full Model List

You can retrieve the complete list of available models using the models endpoint:

GET https://api.mcphub.com/v1/models

Authentication

To use MCPHub's LLM API, you'll need an API key. Here's how to get one:

1

Create an Account

Sign up for a free MCPHub account if you haven't already.

2

Go to Settings

Navigate to Settings → API Keys in your MCPHub dashboard.

3

Generate API Key

Create a new API key and copy it to use in your applications.

🔐 Security Best Practices

  • • Never expose your API key in client-side code
  • • Use environment variables to store your API key
  • • Rotate your API keys regularly
  • • Only grant necessary permissions to your API keys

Streaming Responses

MCPHub supports streaming responses for real-time chat applications. Just add stream: true to your request:

const completion = await client.chat.completions.create({
  model: 'gpt-5-nano',
  messages: [
    {
      role: 'user',
      content: 'Tell me a story'
    }
  ],
  stream: true  // Enable streaming
});

for await (const chunk of completion) {
  const content = chunk.choices[0]?.delta?.content;
  if (content) {
    process.stdout.write(content);
  }
}

Error Handling

MCPHub returns standard HTTP status codes and error messages compatible with OpenRouter's format:

Common Status Codes

  • 200 - Success
  • 400 - Bad Request
  • 401 - Unauthorized
  • 429 - Rate Limited
  • 500 - Server Error

Error Response Format

{
  "error": {
    "message": "Invalid API key",
    "type": "authentication_error",
    "code": "invalid_api_key"
  }
}

Python Error Handling Example

try:
    completion = client.chat.completions.create(
        model="gpt-5-nano",
        messages=[{"role": "user", "content": "Hello!"}]
    )
    print(completion.choices[0].message.content)
    
except Exception as e:
    print(f"Error: {e}")
    # Handle specific error types
    if "authentication" in str(e).lower():
        print("Check your API key")
    elif "rate limit" in str(e).lower():
        print("Rate limit exceeded, please try again later")

Check Your Quota

MCPHub provides API endpoints to check your current credit balance and remaining quota. This is useful for monitoring usage and ensuring you have sufficient credits before making requests.

Quick Quota Check

Use the /quota endpoint for a quick overview of your remaining balance:

GEThttps://api.mcphub.com/quota

Returns your current balance and remaining quota.

curl -X GET "https://api.mcphub.com/quota" \
  -H "X-API-Key: <YOUR_MCPHUB_API_KEY>"

Response:

{
  "username": "your-username",
  "balance": 150.50,
  "remaining_quota": 150.50,
  "used_quota": 0,
  "thinking": {
    "enabled": true,
    "default_budget": 16000,
    "models": ["claude-3-5-sonnet", "claude-3-opus", ...]
  }
}

Detailed Credit Balance

Use the /api/v3/auth/credit-balance endpoint for detailed balance information including expiring credits:

GEThttps://api.mcphub.com/api/v3/auth/credit-balance

Returns detailed credit balance with earning/spending totals and expiration info.

curl -X GET "https://api.mcphub.com/api/v3/auth/credit-balance" \
  -H "X-API-Key: <YOUR_MCPHUB_API_KEY>"

Response:

{
  "user_id": "abc123",
  "current_balance": 150.50,
  "total_earned": 200.00,
  "total_spent": 49.50,
  "tier": "free",
  "expiring_credits": 10.00,
  "next_expiration": "2026-03-05T00:00:00"
}
FieldTypeDescription
user_idstringYour unique user identifier
current_balancefloatYour current available credit balance
total_earnedfloatTotal credits earned (top-ups and grants)
total_spentfloatTotal credits spent on API usage
tierstringAccount tier: free, premium, enterprise, or internal
expiring_creditsfloatCredits expiring within the next 7 days (0.0 if none)
next_expirationstringISO timestamp of next credit expiration (empty if none)

SDK Examples

Python

import requests

response = requests.get(
    "https://api.mcphub.com/api/v3/auth/credit-balance",
    headers={"X-API-Key": "<YOUR_MCPHUB_API_KEY>"}
)

balance = response.json()
print(f"Current balance: {balance['current_balance']}")
print(f"Tier: {balance['tier']}")

if balance["expiring_credits"] > 0:
    print(f"Warning: {balance['expiring_credits']} credits expiring soon")

TypeScript/JavaScript

const response = await fetch("https://api.mcphub.com/api/v3/auth/credit-balance", {
  headers: { "X-API-Key": "<YOUR_MCPHUB_API_KEY>" }
});

const balance = await response.json();
console.log(`Current balance: ${balance.current_balance}`);
console.log(`Tier: ${balance.tier}`);

if (balance.expiring_credits > 0) {
  console.log(`Warning: ${balance.expiring_credits} credits expiring soon`);
}

Insufficient Balance

If your balance is insufficient when making an LLM request, the API returns HTTP 402 Payment Required. You can top up credits from the Billing settings page.

Migrating from OpenRouter

Switching from OpenRouter to MCPHub is straightforward. Here's what you need to change:

Simple 2-Step Migration

1. Update the Base URL
Before (OpenRouter):base_url="https://openrouter.ai/api/v1"
After (MCPHub):base_url="https://api.mcphub.com/v1"
2. Update Your API Key
Before (OpenRouter):api_key="sk-or-v1-..."
After (MCPHub):api_key="your-mcphub-api-key"

✅ That's it! Everything else stays exactly the same - same models, same parameters, same response format.

Next Steps

📚 Explore More

🚀 Get Started