What is Ai Agent Mcp Demo

ai-agent-mcp-demo is a demonstration project that integrates AI agents with the Model Context Protocol (MCP), enabling AI to access specialized tools and real-time data.

Use cases

Use cases include building modular applications that separate AI reasoning from tool execution, enhancing AI capabilities with real-time data, and creating extensible systems that can incorporate new tools without retraining the AI model.

How to use

To use ai-agent-mcp-demo, set up the MCP client, establish server connections, and discover available tools through a standardized interface.

Key features

Key features include dynamic tool discovery, a standardized communication protocol, access to distributed capabilities, improved accuracy with specialized tools, enhanced security through isolated environments, and real-time data access.

Where to use

ai-agent-mcp-demo can be used in various fields such as software development, data analysis, customer support, and any domain requiring the integration of AI with specialized tools.

🤖 Introduction: Integrating AI Agents with MCP

🤔 Why AI Agent Integrate with MCP?

The Model Context Protocol (MCP) addresses a fundamental limitation of AI language models: their inability to directly access specialized tools, real-time data, or external systems. By integrating AI agents with MCP, developers can:

1. Overcome AI Limitations: Bridge the gap between AI’s general knowledge and specific domain capabilities
2. Create Extensible Systems: Add new tools and capabilities without retraining the underlying AI model
3. Build Modular Applications: Separate AI reasoning from specialized tool execution

🌟 Key Benefits of MCP Integration

1. Tool Discovery: AI agents can dynamically discover available tools across multiple servers
2. Standardized Interface: Consistent protocol for AI-to-tool communication
3. Distributed Capabilities: Access specialized tools from various providers through a unified interface
4. Improved Accuracy: Leverage purpose-built tools for specific tasks rather than relying solely on AI approximations
5. Enhanced Security: Tools execute in isolated environments with defined input/output contracts
6. Real-time Data Access: Connect AI responses with current information from external systems

This integration pattern transforms AI from a closed system limited by its training data into an open, extensible platform that can leverage specialized capabilities on demand.

🔄 2 Steps to Integration With MCP Server

1. 🔌 MCP client setup and server connections, tool discovery

   Sequence Chart

   sequenceDiagram
       participant App as Application
       participant Client as MCP Client
       participant Transport as StdioTransport
       participant Server as MCP Server
       
       App->>Client: initMCPServers()
       App->>Transport: new StdioClientTransport(config)
       Transport->>Server: Start server process
       Client->>Transport: client.connect(transport)
       Transport->>Server: Send hello
       Server->>Transport: Hello response
       Transport->>Client: Connection established
       Client->>Server: tools/list request
       Server->>Client: Available tools
       Client->>App: Return McpServer object
       
       Note over App,Server: Server connection active
       
       App->>Client: disconnectAllServers()
       Client->>Transport: transport.close()
       Transport->>Server: Close connection
       Transport->>Client: onclose event
       Client->>App: Server disconnected
   

   Code Example

   import { Client } from "@modelcontextprotocol/sdk/client/index.js";
   import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";
   import { ListToolsResultSchema } from "@modelcontextprotocol/sdk/types.js";
   
   async function connectToMcpServer(serverName, command, args = []) {
   // Create MCP client
   const client = new Client(
       { name: "SimpleMCPClient", version: "1.0.0" },
       { capabilities: {} }
   );
   
   // Create transport layer
   const transport = new StdioClientTransport({
       command: command,
       args: args,
       env: process.env,
       stderr: "pipe",
   });
   
   // Connect to server
   await client.connect(transport);
   
   // Get available tools
   const toolsResponse = await client.request(
       { method: "tools/list" },
       ListToolsResultSchema
   );
   }
   
   

2. 🧠 AI agent interaction

   Sequence Chart

   sequenceDiagram
       participant User
       participant App as Main App
       participant AIAgent as AI Agent
       participant LLM as Language Model
       participant McpClient as MCP Client
       participant Server as MCP Server
       
       User->>App: Query
       App->>AIAgent: processUserQuery(servers, aiModel, query)
       AIAgent->>LLM: generateText(systemPrompt, query)
       LLM->>AIAgent: Initial response with tool calls
       
       Note over AIAgent: Parse tool calls with regex
       
       loop For each tool call
           AIAgent->>McpClient: callMcpTool(servers, serverName, toolName, args)
           McpClient->>Server: tools/call request
           Server->>McpClient: Tool execution result
           McpClient->>AIAgent: Format and return result
       end
       
       AIAgent->>LLM: generateText with tool results
       LLM->>AIAgent: Final response
       AIAgent->>App: Return final response
       App->>User: Display response
   

   Code Example

   import { generateText } from "ai";
   
   // Tool calling pattern in prompt format
   const systemPrompt = `You can use tools with this format:
   <use_mcp_tool>
   <server_name>server_name</server_name>
   <tool_name>tool_name</tool_name>
   <arguments>
   {
   "param1": "value1",
   "param2": "value2"
   }
   </arguments>
   </use_mcp_tool>
   
   Available tools:
   ${servers.map(server => 
   `Server: ${server.name}\nTools: ${server.tools.map(t => 
       `${t.name} - ${t.description || 'No description'}`
   ).join(', ')}`
   ).join('\n')}`;
   
   async function processWithAIAgent(model, servers, userQuery) {
   // Get initial AI response with potential tool calls
   const { text: aiResponse } = await generateText({
       model,
       system: systemPrompt,
       messages: [{ role: "user", content: userQuery }]
   });
   
   // Extract tool calls using regex
   const toolCallRegex = /<use_mcp_tool>[\s\S]*?<server_name>([\s\S]*?)<\/server_name>[\s\S]*?<tool_name>([\s\S]*?)<\/tool_name>[\s\S]*?<arguments>([\s\S]*?)<\/arguments>[\s\S]*?<\/use_mcp_tool>/g;
   const matches = [...aiResponse.matchAll(toolCallRegex)];
   
   // Execute tool calls
   const results = [];
   for (const match of matches) {
       const serverName = match[1].trim();
       const toolName = match[2].trim();
       const args = JSON.parse(match[3].trim());
       
       const server = servers.find(s => s.name === serverName);
       if (server) {
       try {
           // Call the MCP tool
           const result = await server.client.request({
           method: "tools/call",
           params: {
               name: toolName,
               arguments: args
           }
           });
           results.push({ serverName, toolName, result: result.content });
       } catch (error) {
           results.push({ serverName, toolName, error: error.message });
       }
       }
   }
   
   // Generate final response with tool results
   const finalResponse = await generateText({
       model,
       system: systemPrompt,
       messages: [
       { role: "user", content: userQuery },
       { role: "assistant", content: aiResponse },
       { role: "user", content: `Tool results: ${JSON.stringify(results)}` }
       ]
   });
   
   return finalResponse.text;
   }
   

   This code demonstrates how an AI agent integrates with MCP tools through a structured workflow:

   1. 📝 A system prompt informs the AI about available MCP tools and their usage format
   2. 💬 The AI generates a response that may include tool calls in a structured format
   3. 🔍 The code parses these tool calls using regex to extract server name, tool name, and arguments
   4. 🛠️ For each tool call, it finds the appropriate server and executes the requested tool
   5. 📊 The results are collected and sent back to the AI for a final response

   This approach bridges the gap between general AI language capabilities and specialized computing tasks, creating more powerful and useful AI applications.

Run the demo code locally

Check run demo file for more information