initial mcp server setup

intro_test/.env

@@ -0,0 +1 @@
+OPENAI_API_KEY=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

intro_test/.gitignore

@@ -0,0 +1,10 @@
+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv

intro_test/.python-version

@@ -0,0 +1 @@
+3.13

intro_test/Config.py

@@ -0,0 +1,5 @@
+from dotenv import load_dotenv
+import os
+load_dotenv()
+class Config:
+    OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

intro_test/INTRO_README.md

@@ -0,0 +1,295 @@
+# Model Context Protocol (MCP) Developer Guide
+
+## What is MCP?
+
+MCP (Model Context Protocol) is an open-source standard for connecting AI applications to external systems. Using MCP, AI applications like Claude or ChatGPT can connect to data sources (local files, databases), tools (search engines, calculators), and workflows (specialized prompts)—enabling them to access key information and perform tasks.
+
+Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect electronic devices, MCP provides a standardized way to connect AI applications to external systems.
+
+## MCP Overview
+
+![MCP Introduction](images/mcp_intro.png)
+
+The diagram above illustrates how MCP creates a standardized interface between AI models and external tools/data sources through MCP servers that act as intermediaries.
+
+## Core MCP Concepts
+
+MCP servers can provide three main types of capabilities that enable AI applications to interact with external systems:
+
+### 🔧 Tools
+**Functions that can be called by the LLM (with user approval)**
+
+Tools are the primary way AI models perform actions and computations. They represent executable functions that the AI can invoke to:
+
+- **Perform calculations** (like our calculator example)
+- **Execute system commands**
+- **Query databases**
+- **Interact with APIs**
+- **Process data**
+
+**Example Implementation:**
+```python
+@mcp.tool()
+def add(a: int, b: int) -> int:
+    """Add two numbers together"""
+    return a + b
+```
+
+**Key Characteristics:**
+- Require explicit user approval before execution
+- Can have complex input/output schemas
+- Enable AI to perform real-world actions
+- Primary focus of this tutorial
+
+### 📄 Resources
+**File-like data that can be read by clients**
+
+Resources represent data sources that AI can access and read, similar to files or API responses:
+
+- **File contents** (local or remote files)
+- **Database records**
+- **API responses**
+- **Configuration data**
+- **Log files**
+
+**Example Use Cases:**
+- Reading documentation files
+- Accessing configuration settings
+- Querying data from databases
+- Retrieving API responses
+
+### 📝 Prompts
+**Pre-written templates that help users accomplish specific tasks**
+
+Prompts are reusable templates that guide AI behavior for specific scenarios:
+
+- **Task-specific instructions**
+- **Workflow templates**
+- **Specialized conversation starters**
+- **Domain-specific guidance**
+
+**Example Applications:**
+- Code review templates
+- Customer service response formats
+- Technical writing guidelines
+- Analysis frameworks
+
+## Transport Protocols
+
+MCP supports multiple transport protocols for communication between clients and servers:
+
+### 1. Server-Sent Events (SSE)
+- **Use Case**: Web-based applications, remote connections
+- **Communication**: HTTP-based, unidirectional from server to client
+- **Implementation**: Uses HTTP endpoints for real-time communication
+- **Port**: Configurable (default: 8050)
+
+### 2. Standard Input/Output (STDIO)
+- **Use Case**: Local applications, direct process communication
+- **Communication**: Direct process pipes, bidirectional
+- **Implementation**: Launches server as subprocess with stdin/stdout pipes
+- **Setup**: No network configuration required
+
+![Setup Mechanism](images/steup_mechanism.png)
+
+The setup mechanism diagram shows how different transport protocols handle the connection establishment between MCP clients and servers.
+
+## Project Structure
+
+This repository contains example implementations demonstrating MCP concepts:
+
+### `intro_test/` - Basic MCP Implementation
+
+This folder provides fundamental MCP server and client examples:
+
+- **`server.py`** - MCP server with calculator tool using FastMCP
+- **`client_sse.py`** - Client connecting via SSE transport
+- **`client_stdio.py`** - Client connecting via STDIO transport
+- **`main.py`** - Main entry point for testing
+- **`Config.py`** - Configuration management
+
+**Key Features:**
+- Simple calculator tool (`add` function)
+- Support for both SSE and STDIO transports
+- Environment variable configuration
+- FastMCP framework usage
+
+### `intro_test/openai_test/` - OpenAI Integration
+
+This folder demonstrates MCP integration with OpenAI:
+
+- **`client.py`** - MCP-OpenAI client class with tool calling
+- **`server.py`** - MCP server for the integration example
+- **`data/kb.json`** - Knowledge base data for AI responses
+
+**Key Features:**
+- ✅ Seamless OpenAI API integration
+- ✅ Automatic tool discovery and execution
+- ✅ Conversation management with tool results
+- ✅ Error handling and cleanup
+- ✅ Knowledge base tool with JSON data integration
+
+## Quick Start
+
+### Prerequisites
+
+- Python 3.13+
+- OpenAI API key (for OpenAI integration examples)
+
+### Installation
+
+```bash
+# Clone the repository
+git clone <repository-url>
+cd mcp_template
+
+# Navigate to intro_test directory
+cd intro_test
+
+# Install dependencies using uv (recommended)
+uv sync
+
+# Or using pip (if you prefer)
+pip install -e .
+```
+
+### Environment Setup
+
+Create a `.env` file in the project root:
+
+```bash
+# Create .env file
+touch .env
+```
+
+Then add your OpenAI API key to the `.env` file:
+
+```env
+OPENAI_API_KEY=your_openai_api_key_here
+```
+
+**Note:** Get your API key from [OpenAI Platform](https://platform.openai.com/api-keys)
+
+### Running Basic Examples
+
+1. **Start the MCP Server (SSE Transport):**
+```bash
+cd intro_test
+uv run server.py
+```
+
+2. **Run the SSE Client:**
+```bash
+uv run client_sse.py
+```
+
+3. **Run the STDIO Client:**
+```bash
+uv run client_stdio.py
+```
+
+### OpenAI Integration Example
+
+1. **Start the MCP Server:**
+```bash
+cd intro_test
+uv run python openai_test/server.py
+```
+
+2. **Run the OpenAI Client:**
+```bash
+cd intro_test
+uv run python openai_test/client.py
+```
+
+**Note:** The OpenAI integration examples must be run from the `intro_test` directory due to relative import dependencies.
+
+## Understanding the Code
+
+### Server Implementation
+
+The server uses FastMCP framework to create MCP-compatible servers:
+
+```python
+from mcp.server.fastmcp import FastMCP
+
+mcp = FastMCP(
+    name="Calculator",
+    host="0.0.0.0",
+    port=8050,
+    stateless_http=True,
+)
+
+@mcp.tool()
+def add(a: int, b: int) -> int:
+    """Add two numbers together"""
+    return a + b
+
+# Run with different transports
+mcp.run(transport="sse")  # or "stdio" or "streamable-http"
+```
+
+### Client Implementation
+
+Clients connect to servers using different transport methods:
+
+**SSE Client:**
+```python
+async with sse_client("http://localhost:8050/sse") as (read_stream, write_stream):
+    async with ClientSession(read_stream, write_stream) as session:
+        await session.initialize()
+        # Use tools...
+```
+
+**STDIO Client:**
+```python
+server_params = StdioServerParameters(
+    command="python",
+    args=["server.py"],
+)
+async with stdio_client(server_params) as (read_stream, write_stream):
+    async with ClientSession(read_stream, write_stream) as session:
+        # Use tools...
+```
+
+### OpenAI Integration
+
+The OpenAI client bridges MCP tools with OpenAI's function calling:
+
+```python
+# Get MCP tools in OpenAI format
+tools = await self.get_mcp_tools()
+
+# Make OpenAI API call with tools
+response = await self.openai_client.chat.completions.create(
+    model=self.model,
+    messages=[{"role": "user", "content": query}],
+    tools=tools,
+    tool_choice="auto",
+)
+```
+
+## Next Steps
+
+This guide covers the fundamentals of MCP development. The examples in this repository demonstrate:
+
+- ✅ Basic server and client setup
+- ✅ Multiple transport protocol implementations
+- ✅ OpenAI integration patterns
+- ✅ Tool definition and execution
+- ✅ Error handling and cleanup
+
+Future enhancements may include:
+- Additional transport protocols
+- Advanced tool implementations
+- Database integrations
+- Webhook support
+- Authentication mechanisms
+
+---
+
+*This documentation will be updated as new features and examples are added to the repository.*
+
+
+
+URL: https://modelcontextprotocol.io/docs/learn/architecture

intro_test/client_sse.py

@@ -0,0 +1,38 @@
+import asyncio
+import nest_asyncio
+from mcp import ClientSession
+from mcp.client.sse import sse_client
+
+nest_asyncio.apply()  # Needed to run interactive python
+
+"""
+Make sure:
+1. The server is running before running this script.
+2. The server is configured to use SSE transport.
+3. The server is listening on port 8050.
+
+To run the server:
+uv run server.py
+"""
+
+
+async def main():
+    # Connect to the server using SSE
+    async with sse_client("http://localhost:8050/sse") as (read_stream, write_stream):
+        async with ClientSession(read_stream, write_stream) as session:
+            # Initialize the connection
+            await session.initialize()
+
+            # List available tools
+            tools_result = await session.list_tools()
+            print("Available tools:")
+            for tool in tools_result.tools:
+                print(f"  - {tool.name}: {tool.description}")
+
+            # Call our calculator tool
+            result = await session.call_tool("add", arguments={"a": 2, "b": 3})
+            print(f"2 + 3 = {result.content[0].text}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

intro_test/client_stdio.py

@@ -0,0 +1,34 @@
+import asyncio
+import nest_asyncio
+from mcp import ClientSession, StdioServerParameters
+from mcp.client.stdio import stdio_client
+
+nest_asyncio.apply()  # Needed to run interactive python
+
+
+async def main():
+    # Define server parameters
+    server_params = StdioServerParameters(
+        command="python",  # The command to run your server
+        args=["server.py"],  # Arguments to the command
+    )
+
+    # Connect to the server
+    async with stdio_client(server_params) as (read_stream, write_stream):
+        async with ClientSession(read_stream, write_stream) as session:
+            # Initialize the connection
+            await session.initialize()
+
+            # List available tools
+            tools_result = await session.list_tools()
+            print("Available tools:")
+            for tool in tools_result.tools:
+                print(f"  - {tool.name}: {tool.description}")
+
+            # Call our calculator tool
+            result = await session.call_tool("add", arguments={"a": 2, "b": 3})
+            print(f"2 + 3 = {result.content[0].text}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

intro_test/main.py

@@ -0,0 +1,6 @@
+def main():
+    print("Hello from intro-test!")
+
+
+if __name__ == "__main__":
+    main()

intro_test/openai_test/client.py

@@ -0,0 +1,176 @@
+import asyncio
+import json
+import sys
+import os
+from contextlib import AsyncExitStack
+from typing import Any, Dict, List, Optional
+
+import nest_asyncio
+from dotenv import load_dotenv
+from mcp import ClientSession, StdioServerParameters
+from mcp.client.stdio import stdio_client
+from openai import AsyncOpenAI
+
+# Add parent directory to path to import Config
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from Config import Config
+
+# Apply nest_asyncio to allow nested event loops (needed for Jupyter/IPython)
+nest_asyncio.apply()
+
+
+
+class MCPOpenAIClient:
+    """Client for interacting with OpenAI models using MCP tools."""
+
+    def __init__(self, model: str = "gpt-4o"):
+        """Initialize the OpenAI MCP client.
+
+        Args:
+            model: The OpenAI model to use.
+        """
+        # Initialize session and client objects
+        self.session: Optional[ClientSession] = None
+        self.exit_stack = AsyncExitStack()
+        self.openai_client = AsyncOpenAI(api_key=Config.OPENAI_API_KEY)
+        self.model = model
+        self.stdio: Optional[Any] = None
+        self.write: Optional[Any] = None
+
+    async def connect_to_server(self, server_script_path: str = "server.py"):
+        """Connect to an MCP server.
+
+        Args:
+            server_script_path: Path to the server script.
+        """
+        # Server configuration
+        server_params = StdioServerParameters(
+            command="python",
+            args=[server_script_path],
+        )
+
+        # Connect to the server
+        stdio_transport = await self.exit_stack.enter_async_context(
+            stdio_client(server_params)
+        )
+        self.stdio, self.write = stdio_transport
+        self.session = await self.exit_stack.enter_async_context(
+            ClientSession(self.stdio, self.write)
+        )
+
+        # Initialize the connection
+        await self.session.initialize()
+
+        # List available tools
+        tools_result = await self.session.list_tools()
+        print("\nConnected to server with tools:")
+        for tool in tools_result.tools:
+            print(f"  - {tool.name}: {tool.description}")
+
+    async def get_mcp_tools(self) -> List[Dict[str, Any]]:
+        """Get available tools from the MCP server in OpenAI format.
+
+        Returns:
+            A list of tools in OpenAI format.
+        """
+        tools_result = await self.session.list_tools()
+        return [
+            {
+                "type": "function",
+                "function": {
+                    "name": tool.name,
+                    "description": tool.description,
+                    "parameters": tool.inputSchema,
+                },
+            }
+            for tool in tools_result.tools
+        ]
+
+    async def process_query(self, query: str) -> str:
+        """Process a query using OpenAI and available MCP tools.
+
+        Args:
+            query: The user query.
+
+        Returns:
+            The response from OpenAI.
+        """
+        # Get available tools
+        tools = await self.get_mcp_tools()
+
+        # Initial OpenAI API call
+        response = await self.openai_client.chat.completions.create(
+            model=self.model,
+            messages=[{"role": "user", "content": query}],
+            tools=tools,
+            tool_choice="auto",
+        )
+
+        # Get assistant's response
+        assistant_message = response.choices[0].message
+
+        # Initialize conversation with user query and assistant response
+        messages = [
+            {"role": "user", "content": query},
+            assistant_message,
+        ]
+
+        # Handle tool calls if present
+        if assistant_message.tool_calls:
+            # Process each tool call
+            for tool_call in assistant_message.tool_calls:
+                # Execute tool call
+                result = await self.session.call_tool(
+                    tool_call.function.name,
+                    arguments=json.loads(tool_call.function.arguments),
+                )
+
+                # Add tool response to conversation
+                messages.append(
+                    {
+                        "role": "tool",
+                        "tool_call_id": tool_call.id,
+                        "content": result.content[0].text,
+                    }
+                )
+
+            # Get final response from OpenAI with tool results
+            final_response = await self.openai_client.chat.completions.create(
+                model=self.model,
+                messages=messages,
+                tools=tools,
+                tool_choice="none",  # Don't allow more tool calls
+            )
+
+            return final_response.choices[0].message.content
+
+        # No tool calls, just return the direct response
+        return assistant_message.content
+
+    async def cleanup(self):
+        """Clean up resources."""
+        try:
+            await self.exit_stack.aclose()
+        except Exception as e:
+            # Ignore cleanup errors that don't affect functionality
+            pass
+
+
+async def main():
+    """Main entry point for the client."""
+    client = MCPOpenAIClient()
+    try:
+        await client.connect_to_server("server.py")
+
+        # Example: Ask about company vacation policy
+        query = "How many employees do we have?"
+        print(f"\nQuery: {query}")
+
+        response = await client.process_query(query)
+        print(f"\nResponse: {response}")
+    finally:
+        await client.cleanup()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

intro_test/openai_test/server.py

@@ -0,0 +1,53 @@
+import os
+import json
+from mcp.server.fastmcp import FastMCP
+
+# Create an MCP server
+mcp = FastMCP(
+    name="Knowledge Base",
+    host="0.0.0.0",  # only used for SSE transport (localhost)
+    port=8050,  # only used for SSE transport (set this to any port)
+)
+
+
+@mcp.tool()
+def get_knowledge_base() -> str:
+    """Retrieve the entire knowledge base as a formatted string.
+
+    Returns:
+        A formatted string containing all Q&A pairs from the knowledge base.
+    """
+    try:
+        kb_path = os.path.join(os.path.dirname(__file__), "data", "kb.json")
+        with open(kb_path, "r") as f:
+            kb_data = json.load(f)
+
+        # Format the knowledge base as a string
+        kb_text = "Here is the retrieved knowledge base:\n\n"
+
+        if isinstance(kb_data, list):
+            for i, item in enumerate(kb_data, 1):
+                if isinstance(item, dict):
+                    question = item.get("question", "Unknown question")
+                    answer = item.get("answer", "Unknown answer")
+                else:
+                    question = f"Item {i}"
+                    answer = str(item)
+
+                kb_text += f"Q{i}: {question}\n"
+                kb_text += f"A{i}: {answer}\n\n"
+        else:
+            kb_text += f"Knowledge base content: {json.dumps(kb_data, indent=2)}\n\n"
+
+        return kb_text
+    except FileNotFoundError:
+        return "Error: Knowledge base file not found"
+    except json.JSONDecodeError:
+        return "Error: Invalid JSON in knowledge base file"
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+# Run the server
+if __name__ == "__main__":
+    mcp.run(transport="stdio")

intro_test/pyproject.toml

@@ -0,0 +1,13 @@
+[project]
+name = "intro-test"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "mcp[cli]>=1.13.1",
+    "nest-asyncio",
+    "openai>=1.0.0",
+    "pytest>=8.4.2",
+    "python-dotenv",
+]

intro_test/server.py

@@ -0,0 +1,35 @@
+from mcp.server.fastmcp import FastMCP
+from dotenv import load_dotenv
+
+load_dotenv("../.env")
+
+# Create an MCP server
+mcp = FastMCP(
+    name="Calculator",
+    host="0.0.0.0",  # only used for SSE transport (localhost)
+    port=8050,  # only used for SSE transport (set this to any port)
+    stateless_http=True,
+)
+
+
+# Add a simple calculator tool
+@mcp.tool()
+def add(a: int, b: int) -> int:
+    """Add two numbers together"""
+    return a + b
+
+
+# Run the server
+if __name__ == "__main__":
+    transport = "sse"
+    if transport == "stdio":
+        print("Running server with stdio transport")
+        mcp.run(transport="stdio")
+    elif transport == "sse":
+        print("Running server with SSE transport")
+        mcp.run(transport="sse")
+    elif transport == "streamable-http":
+        print("Running server with Streamable HTTP transport")
+        mcp.run(transport="streamable-http")
+    else:
+        raise ValueError(f"Unknown transport: {transport}")

intro_test/uv.lock

@@ -0,0 +1,624 @@
+version = 1
+revision = 2
+requires-python = ">=3.13"
+
+[[package]]
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+version = "0.7.0"
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+]
+
+[[package]]
+name = "anyio"
+version = "4.10.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "idna" },
+    { name = "sniffio" },
+]
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+]
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+[[package]]
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+version = "25.3.0"
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