🧩 MCP Demo (Model + Tool + Context)

A lightweight Node.js demonstration of MCP using OpenAI function-calling to show how LLMs can use external tools with proper context management.

📦 Project Structure

mcp-demo/
├── package.json
├── src/
│   ├── app.js                          # Entry point
│   ├── llm/
│   │   └── openai.js                   # OpenAI Chat Completions API
│   ├── mcp/
│   │   ├── context.js                  # Context builder + tool result enrichment
│   │   ├── orchestrator.js             # MCP main workflow (runMCP)
│   │   ├── executor.js                 # Executes validated tool calls
│   │   └── toolRegistry.js             # Tool list → OpenAI tool schemas
│   ├── tools/
│   │   ├── revenueTool.js              # get_revenue function
│   │   └── timeTool.js                 # get_time function
│   └── utils/
│       └── logger.js                   # Logging utilities
└── README.md

🚀 Quick Start

1. Install dependencies

npm install

2. Set OpenAI API key

export OPENAI_API_KEY=your_api_key_here

3. Run a demo

Demo 1

node src/app.js "Doanh thu tháng 02/2025 là bao nhiêu?"

Expected output:

LLM Response: {"index":0,"message":{"role":"assistant","content":null,"tool_calls":[{"id":"call_...","type":"function","function":{"name":"get_revenue","arguments":"{\"month\":\"02/2025\"}"}}],"finish_reason":"tool_calls"}

LLM Response after tool call: {"index":0,"message":{"role":"assistant","content":"Doanh thu tháng 02/2025 là 150.000.","finish_reason":"stop"},...}

Final Answer:
 Doanh thu tháng 02/2025 là 150.000.

Demo 2 - Tool call 2 times

node src/app.js "Doanh thu tháng này là bao nhiêu?"

Expected output:

LLM Response: {"index":0,"message":{"role":"assistant","content":null,"tool_calls":[{"id":"call_CyQQAsdYY3c0vl53ilbDdgAv","type":"function","function":{"name":"get_time","arguments":"{}"}}],"refusal":null,"annotations":[]},"logprobs":null,"finish_reason":"tool_calls"}
LLM Response after tool call: {"index":0,"message":{"role":"assistant","content":null,"tool_calls":[{"id":"call_qfY30jluw01Ltq4QMqX70Q0j","type":"function","function":{"name":"get_revenue","arguments":"{\"month\":\"03/2026\"}"}}],"refusal":null,"annotations":[]},"logprobs":null,"finish_reason":"tool_calls"}
LLM Response after tool call: {"index":0,"message":{"role":"assistant","content":"Doanh thu tháng 03/2026 là 150,000.","refusal":null,"annotations":[]},"logprobs":null,"finish_reason":"stop"}

Final Answer:
 Doanh thu tháng 03/2026 là 150,000.

🧠 Core Components

src/mcp/context.js

Manages contextual information and tool result enrichment:

• buildInitialContext({ userId, locale, appState })

  • Builds system message with user metadata, business rules, and tool usage examples
  • Returns locale-aware context for LLM instructions

• enrichConversationWithToolResult(conversation, { toolName, result, toolCallId })

  • Appends tool response to conversation history
  • Maintains proper message chain for multi-turn tool use

• resolveLocaleText(locale, key)

  • Retrieves locale-specific messages (Vietnamese, English)
  • Supports extensibility for new languages

src/mcp/orchestrator.js

Main MCP workflow (runMCP):

1. Build context - Initialize system message with user locale and business rules
2. Call LLM - Send user query with available tools
3. Check for tool calls - If LLM wants to use a tool:
   • Extract tool name and arguments
   • Execute the tool
   • Append tool result to conversation
   • Call LLM again with result
4. Return answer - Final text response from LLM

src/mcp/executor.js

Tool execution pipeline:

• Retrieves tool definition from registry
• Validates arguments against Zod schema
• Executes tool handler
• Returns result or error

src/mcp/toolRegistry.js

Tool management:

• Maintains list of all available tools
• Converts tool definitions to OpenAI-compatible schemas
• Provides tool lookup by name

src/tools/revenueTool.js

Example revenue data tool:

{
  name: "get_revenue",
  description: "Get revenue for a specific month in MM/YYYY format",
  schema: z.object({
    month: z.string().describe("Month in MM/YYYY format (e.g., '02/2025')")
  }),
  handler: async ({ month }) => { /* returns revenue data */ }
}

src/llm/openai.js

OpenAI integration:

• Configures GPT-4o-mini model
• Handles tool schema formatting
• Manages function-calling flow

🧪 Demo Usage Examples

Example 1: Revenue query with month

node src/app.js "Doanh thu tháng 02/2025 là bao nhiêu?"

Flow:

• LLM identifies month 02/2025 from Vietnamese query
• Calls get_revenue with {"month":"02/2025"}
• Returns formatted revenue

Example 2: Current time query

node src/app.js "Hôm nay là ngày mấy"

Flow:

• LLM recognizes date query
• May call time tool if configured
• Returns current date response

Example 3: Query with missing context

node src/app.js "Doanh thu là bao nhiêu?"

Expected:

• Context rules ask for month clarification
• LLM prompts user for MM/YYYY format

🛠️ How MCP Works: The Flow

User Input
    ↓
[1] buildInitialContext
    ↓
[2] callLLM with tools
    ↓
    └─→ LLM response: tool_calls?
         ├─ YES: [3] executeTool
         │        ↓
         │        [4] enrichConversationWithToolResult
         │        ↓
         │        [2] callLLM again (with tool result)
         │
         └─ NO: Return final_response

🛡️ Key Features

✅ Context Injection

System context includes:

• User locale and preferences
• Business rules (e.g., month format requirements)
• Tool usage examples
• Metadata (userId, environment)

✅ Schema Validation

• Zod schemas enforce type safety
• Invalid arguments rejected before execution
• Clear error messages

✅ Tool Result Feedback Loop

• Tool output automatically appended to conversation
• LLM can reason over tool results
• Supports multi-turn tool usage

✅ Multilingual Support

• Vietnamese and English prompts
• Extensible locale resolver
• Business rule enforcement per locale

🔧 Extending the Demo

Add a new tool

1. Create src/tools/myTool.js:

import { z } from "zod";

export const myTool = {
  name: "my_function",
  description: "What this tool does",
  schema: z.object({
    param1: z.string().describe("Parameter description"),
  }),
  handler: async ({ param1 }) => {
    return { result: "data" };
  },
};

2. Register in src/mcp/toolRegistry.js:

import { myTool } from "../tools/myTool.js";

export const tools = [revenueTool, timeTool, myTool];

Improve month validation

In revenueTool.js, add regex validation:

schema: z.object({
  month: z
    .string()
    .regex(/^(0[1-9]|1[0-2])\/\d{4}$/, "Format must be MM/YYYY")
    .describe("Month in MM/YYYY format"),
})

Connect real database

Modify revenueTool.handler:

handler: async ({ month }) => {
  const data = await db.query("SELECT * FROM revenue WHERE month = ?", [month]);
  return { month, revenue: data[0].revenue };
}

Add authentication

In orchestrator.js:

const context = buildInitialContext({
  userId: req.user.id,
  locale: req.user.locale,
  appState: { environment: "prod" }
});

📊 Architecture Diagram

┌─────────────────────────────────────────────────────┐
│  User Query (Vietnamese/English)                    │
└──────────────────┬──────────────────────────────────┘
                   ↓
┌─────────────────────────────────────────────────────┐
│  orchestrator.js: runMCP()                          │
│  - buildInitialContext()                            │
│  - Add system message + user query                  │
└──────────────────┬──────────────────────────────────┘
                   ↓
┌─────────────────────────────────────────────────────┐
│  openai.js: callLLM()                               │
│  - Send to GPT-4o-mini with tool schemas            │
└──────────────────┬──────────────────────────────────┘
                   ↓
         ┌─────────┴─────────┐
         ↓                   ↓
   [Tool Calls]        [Direct Answer]
         ↓                   ↓
    executor.js          RETURN
  - Resolve tool
  - Validate args
  - Execute handler
         ↓
    enrichConversationWithToolResult()
         ↓
    callLLM() again
         ↓
    Return final answer

🧾 Troubleshooting

Error: Invalid input expected string, received undefined

Cause: LLM did not provide required parameters
Solution: Improve system prompt with clear examples

Error: Invalid parameter: messages with role 'tool' must be a response to preceeding message with tool_calls

Cause: Message chain broken, tool message without matching assistant message
Solution: Ensure enrichConversationWithToolResult is called with full conversation history including assistant message

Error: 401 Unauthorized when calling OpenAI

Cause: Invalid or missing API key
Solution:

export OPENAI_API_KEY=sk-...
echo $OPENAI_API_KEY  # verify it's set

LLM not using tools

Cause: Tool schema not properly formatted or example not clear
Solution:

• Check Zod schema conversion in toolRegistry.js
• Add explicit tool usage example in context.js systemMessage
• Verify tool names are included in OpenAI request

📝 Recommended Next Steps

• [ ] Add persistent conversation history (database)
• [ ] Implement rate limiting for API calls
• [ ] Add monitoring/logging for tool execution
• [ ] Create integration tests for MCP flow
• [ ] Add TypeScript for type safety
• [ ] Document cost estimation per tool call
• [ ] Implement retry logic for failed tool calls
• [ ] Add support for streaming responses

🏗️ Technology Stack

• Runtime: Node.js v24+
• LLM: OpenAI GPT-4o-mini
• Schema Validation: Zod
• Environment: dotenv

📄 License

MIT

🤝 Contributing

Contributions welcome! Follow these patterns:

• Use Zod for schema definitions
• Keep tool handlers pure and side-effect-free
• Test tool outputs match expected schema
• Update README with new tool examples

Made with ❤️ for MCP demos