MCP Examples

Complete examples of MCP tools in action.

Order Management Example

Tool Schema

{
  "name": "get_order_status",
  "description": "Get order status and details",
  "parameters": {
    "type": "object",
    "properties": {
      "order_id": {
        "type": "string",
        "description": "Order ID",
        "required": true
      }
    }
  }
}

Use in Agent

import requests

# Create agent with order tool
response = requests.post(
    "http://localhost/api/v1/agent/configs",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    json={
        "name": "order_agent",
        "system_prompt": "Help users check order status and manage orders.",
        "model": "gpt-4",
        "builtin_tools": [],
        "mcp_servers": ["ORDER_API"],
        "tool_choice": "auto",
        "max_iterations": 3
    }
)

agent_id = response.json()["id"]

# Chat with agent
chat_response = requests.post(
    "http://localhost/api/v1/agent/chat",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    json={
        "agent_id": agent_id,
        "message": "What's the status of order #12345?"
    }
)

result = chat_response.json()
print(f"Response: {result['response']}")
print(f"Tool used: {result['tool_calls'][0]['tool_name']}")

Weather Service Example

Tool Schema

{
  "name": "get_weather",
  "description": "Get current weather and forecast",
  "parameters": {
    "type": "object",
    "properties": {
      "location": {
        "type": "string",
        "description": "City name or postal code",
        "required": true
      },
      "units": {
        "type": "string",
        "enum": ["celsius", "fahrenheit"],
        "default": "celsius"
      }
    }
  }
}

Use in Agent

import requests

# Create agent with weather tool
response = requests.post(
    "http://localhost/api/v1/agent/configs",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    json={
        "name": "weather_agent",
        "system_prompt": "Provide current weather information and forecasts.",
        "model": "gpt-4",
        "builtin_tools": [],
        "mcp_servers": ["WEATHER"],
        "tool_choice": "auto",
        "max_iterations": 2
    }
)

agent_id = response.json()["id"]

# Chat with agent
chat_response = requests.post(
    "http://localhost/api/v1/agent/chat",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    json={
        "agent_id": agent_id,
        "message": "What's the weather like in New York?"
    }
)

result = chat_response.json()
print(f"Response: {result['response']}")

CRM Integration Example

Tool Schema

{
  "name": "get_customer_info",
  "description": "Retrieve customer information from CRM",
  "parameters": {
    "type": "object",
    "properties": {
      "customer_id": {
        "type": "string",
        "description": "Customer ID",
        "required": true
      },
      "include_orders": {
        "type": "boolean",
        "default": true
      }
    }
  }
}

Use in Agent

import requests

# Create agent with CRM tool
response = requests.post(
    "http://localhost/api/v1/agent/configs",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    json={
        "name": "support_agent",
        "system_prompt": "Help customers with their account and orders.",
        "model": "gpt-4",
        "builtin_tools": [],
        "mcp_servers": ["CRM"],
        "tool_choice": "auto",
        "max_iterations": 5
    }
)

agent_id = response.json()["id"]

# Chat with agent
chat_response = requests.post(
    "http://localhost/api/v1/agent/chat",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    json={
        "agent_id": agent_id,
        "message": "When was my last order and what was it?"
    }
)

result = chat_response.json()
print(f"Response: {result['response']}")

Multi-Tool Agent Example

Configure Multiple MCP Servers

# In .env file
MCP_ORDER_API_URL=https://orders.api.com
MCP_ORDER_API_KEY=order_key
MCP_WEATHER_URL=https://weather.api.com
MCP_WEATHER_KEY=weather_key
MCP_CRM_URL=https://crm.api.com
MCP_CRM_TOKEN=crm_token

Create Agent

import requests

response = requests.post(
    "http://localhost/api/v1/agent/configs",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    json={
        "name": "full_support_agent",
        "system_prompt": "Comprehensive support agent with access to orders, weather, and customer data.",
        "model": "gpt-4",
        "builtin_tools": [],
        "mcp_servers": ["ORDER_API", "WEATHER", "CRM"],
        "tool_choice": "auto",
        "max_iterations": 10
    }
)

agent_id = response.json()["id"]

Complex Query

# Query that uses multiple tools
chat_response = requests.post(
    "http://localhost/api/v1/agent/chat",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    json={
        "agent_id": agent_id,
        "message": "Check order #12345 status. If it's shipped, will the weather affect delivery?"
    }
)

result = chat_response.json()
print(f"Response: {result['response']}")

# Show tools used
print("\nTools used:")
for tool_call in result.get('tool_calls', []):
    print(f"  - {tool_call['tool_name']}")

Custom Data Analysis Tool

Tool Schema

{
  "name": "analyze_sales_data",
  "description": "Analyze sales data from internal database",
  "parameters": {
    "type": "object",
    "properties": {
      "time_range": {
        "type": "string",
        "enum": ["week", "month", "quarter", "year"],
        "required": true
      },
      "metrics": {
        "type": "array",
        "description": "Metrics to calculate",
        "default": ["total_sales", "avg_order_value", "top_products"]
      }
    }
  }
}

Use in Agent

import requests

# Create agent
response = requests.post(
    "http://localhost/api/v1/agent/configs",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    json={
        "name": "analytics_agent",
        "system_prompt": "Analyze sales data and provide insights.",
        "model": "gpt-4",
        "builtin_tools": [],
        "mcp_servers": ["ANALYTICS"],
        "tool_choice": "auto",
        "max_iterations": 5
    }
)

agent_id = response.json()["id"]

# Query analytics
chat_response = requests.post(
    "http://localhost/api/v1/agent/chat",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    json={
        "agent_id": agent_id,
        "message": "What are our top selling products this month?"
    }
)

result = chat_response.json()
print(f"Analysis: {result['response']}")

React Integration

import React, { useState } from 'react';

function MCPSupport() {
  const [agentId, setAgentId] = useState('');
  const [message, setMessage] = useState('');
  const [response, setResponse] = useState('');
  const [loading, setLoading] = useState(false);

  const createAgent = async () => {
    try {
      const res = await fetch('http://localhost/api/v1/agent/configs', {
        method: 'POST',
        headers: {
          'Authorization': 'Bearer YOUR_API_KEY',
          'Content-Type': 'application/json'
        },
        body: JSON.stringify({
          name: 'order_agent',
          system_prompt: 'Help users with order inquiries.',
          model: 'gpt-4',
          builtin_tools: [],
          mcp_servers: ['ORDER_API'],
          tool_choice: 'auto',
          max_iterations: 3
        })
      });

      const agent = await res.json();
      setAgentId(agent.id);
    } catch (error) {
      console.error('Error creating agent:', error);
    }
  };

  const sendMessage = async () => {
    if (!message.trim() || loading) return;

    setLoading(true);
    try {
      const res = await fetch('http://localhost/api/v1/agent/chat', {
        method: 'POST',
        headers: {
          'Authorization': 'Bearer YOUR_API_KEY',
          'Content-Type': 'application/json'
        },
        body: JSON.stringify({
          agent_id: agentId,
          message
        })
      });

      const result = await res.json();
      setResponse(result.response);
    } catch (error) {
      console.error('Error:', error);
    } finally {
      setLoading(false);
    }
  };

  return (
    <div className="p-6">
      {!agentId ? (
        <button
          onClick={createAgent}
          className="px-4 py-2 bg-blue-500 text-white rounded"
        >
          Create Agent
        </button>
      ) : (
        <div>
          <textarea
            value={message}
            onChange={(e) => setMessage(e.target.value)}
            placeholder="Ask about your order..."
            className="w-full p-2 border rounded"
            rows={3}
          />

          <button
            onClick={sendMessage}
            disabled={loading || !message}
            className="mt-2 px-4 py-2 bg-green-500 text-white rounded"
          >
            {loading ? 'Sending...' : 'Send'}
          </button>

          {response && (
            <div className="mt-4 p-4 bg-gray-100 rounded">
              <h3 className="font-bold mb-2">Response:</h3>
              <p>{response}</p>
            </div>
          )}
        </div>
      )}
    </div>
  );
}

export default MCPSupport;

Error Handling Example

import requests
from requests.exceptions import RequestException

def safe_mcp_chat(agent_id, message, max_retries=3):
    for attempt in range(max_retries):
        try:
            response = requests.post(
                "http://localhost/api/v1/agent/chat",
                headers={"Authorization": "Bearer YOUR_API_KEY"},
                json={
                    "agent_id": agent_id,
                    "message": message
                },
                timeout=30
            )

            response.raise_for_status()
            return response.json()

        except RequestException as e:
            if attempt == max_retries - 1:
                raise
            print(f"Attempt {attempt + 1} failed. Retrying...")
            time.sleep(2 ** attempt)

# Use with error handling
try:
    result = safe_mcp_chat(agent_id, "Check order status")
    print(f"Response: {result['response']}")
except Exception as e:
    print(f"All attempts failed: {e}")

Best Practices

Clear tool names - Use descriptive, action-oriented names
Detailed descriptions - Explain tool purpose and parameters
Error messages - Provide helpful error information
Retry logic - Handle transient failures gracefully
Testing - Test tools thoroughly in production
Monitoring - Track tool usage and performance

Next Steps

Setup - Configure MCP servers
Creating Tools - Build your own tools
Agent Documentation - Integrate MCP with agents

Order Management Example​

Tool Schema​

Use in Agent​

Weather Service Example​

Tool Schema​

Use in Agent​

CRM Integration Example​

Tool Schema​

Use in Agent​

Multi-Tool Agent Example​

Configure Multiple MCP Servers​

Create Agent​

Complex Query​

Custom Data Analysis Tool​

Tool Schema​

Use in Agent​

React Integration​

Error Handling Example​

Best Practices​

Next Steps​

Order Management Example

Tool Schema

Use in Agent

Weather Service Example

Tool Schema

Use in Agent

CRM Integration Example

Tool Schema

Use in Agent

Multi-Tool Agent Example

Configure Multiple MCP Servers

Create Agent

Complex Query

Custom Data Analysis Tool

Tool Schema

Use in Agent

React Integration

Error Handling Example

Best Practices

Next Steps