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‎MCP Codes/Model_Context_Protocol_Integration_Marktechpost.ipynb‎

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "code",
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"source": [
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"import json\n",
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"import asyncio\n",
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"from dataclasses import dataclass, asdict\n",
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"from typing import Dict, List, Any, Optional, Callable\n",
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"from datetime import datetime\n",
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"import random\n",
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"\n",
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"@dataclass\n",
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"class Resource:\n",
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" uri: str\n",
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" name: str\n",
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" description: str\n",
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" mime_type: str\n",
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" content: Any = None\n",
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"\n",
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"@dataclass\n",
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"class Tool:\n",
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" name: str\n",
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" description: str\n",
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" parameters: Dict[str, Any]\n",
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" handler: Optional[Callable] = None\n",
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"\n",
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"@dataclass\n",
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"class Message:\n",
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" role: str\n",
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" content: str\n",
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" timestamp: str = None\n",
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" def __post_init__(self):\n",
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" if not self.timestamp:\n",
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" self.timestamp = datetime.now().isoformat()"
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],
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"metadata": {
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"id": "9e7Z197gbkhs"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"class MCPServer:\n",
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" def __init__(self, name: str):\n",
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" self.name = name\n",
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" self.resources: Dict[str, Resource] = {}\n",
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" self.tools: Dict[str, Tool] = {}\n",
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" self.capabilities = {\"resources\": True, \"tools\": True, \"prompts\": True, \"logging\": True}\n",
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" print(f\"✓ MCP Server '{name}' initialized with capabilities: {list(self.capabilities.keys())}\")\n",
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" def register_resource(self, resource: Resource) -> None:\n",
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" self.resources[resource.uri] = resource\n",
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" print(f\" → Resource registered: {resource.name} ({resource.uri})\")\n",
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" def register_tool(self, tool: Tool) -> None:\n",
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" self.tools[tool.name] = tool\n",
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" print(f\" → Tool registered: {tool.name}\")\n",
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" async def get_resource(self, uri: str) -> Optional[Resource]:\n",
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" await asyncio.sleep(0.1)\n",
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" return self.resources.get(uri)\n",
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" async def execute_tool(self, tool_name: str, arguments: Dict[str, Any]) -> Any:\n",
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" if tool_name not in self.tools:\n",
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" raise ValueError(f\"Tool '{tool_name}' not found\")\n",
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" tool = self.tools[tool_name]\n",
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" if tool.handler:\n",
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" return await tool.handler(**arguments)\n",
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" return {\"status\": \"executed\", \"tool\": tool_name, \"args\": arguments}\n",
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" def list_resources(self) -> List[Dict[str, str]]:\n",
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" return [{\"uri\": r.uri, \"name\": r.name, \"description\": r.description} for r in self.resources.values()]\n",
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" def list_tools(self) -> List[Dict[str, Any]]:\n",
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" return [{\"name\": t.name, \"description\": t.description, \"parameters\": t.parameters} for t in self.tools.values()]"
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],
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"metadata": {
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"id": "O-DOPi4jbkVF"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"class MCPClient:\n",
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" def __init__(self, client_id: str):\n",
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" self.client_id = client_id\n",
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" self.connected_servers: Dict[str, MCPServer] = {}\n",
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" self.context: List[Message] = []\n",
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" print(f\"\\n✓ MCP Client '{client_id}' initialized\")\n",
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" def connect_server(self, server: MCPServer) -> None:\n",
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" self.connected_servers[server.name] = server\n",
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" print(f\" → Connected to server: {server.name}\")\n",
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" async def query_resources(self, server_name: str) -> List[Dict[str, str]]:\n",
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" if server_name not in self.connected_servers:\n",
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" raise ValueError(f\"Not connected to server: {server_name}\")\n",
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" return self.connected_servers[server_name].list_resources()\n",
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" async def fetch_resource(self, server_name: str, uri: str) -> Optional[Resource]:\n",
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" if server_name not in self.connected_servers:\n",
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" raise ValueError(f\"Not connected to server: {server_name}\")\n",
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" server = self.connected_servers[server_name]\n",
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" resource = await server.get_resource(uri)\n",
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" if resource:\n",
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" self.add_to_context(Message(role=\"system\", content=f\"Fetched resource: {resource.name}\"))\n",
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" return resource\n",
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" async def call_tool(self, server_name: str, tool_name: str, **kwargs) -> Any:\n",
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" if server_name not in self.connected_servers:\n",
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" raise ValueError(f\"Not connected to server: {server_name}\")\n",
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" server = self.connected_servers[server_name]\n",
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" result = await server.execute_tool(tool_name, kwargs)\n",
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" self.add_to_context(Message(role=\"system\", content=f\"Tool '{tool_name}' executed\"))\n",
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" return result\n",
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" def add_to_context(self, message: Message) -> None:\n",
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" self.context.append(message)\n",
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" def get_context(self) -> List[Dict[str, Any]]:\n",
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" return [asdict(msg) for msg in self.context]"
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],
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"metadata": {
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"id": "_aMmCipbbkP7"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"async def analyze_sentiment(text: str) -> Dict[str, Any]:\n",
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" await asyncio.sleep(0.2)\n",
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" sentiments = [\"positive\", \"negative\", \"neutral\"]\n",
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" return {\"text\": text, \"sentiment\": random.choice(sentiments), \"confidence\": round(random.uniform(0.7, 0.99), 2)}\n",
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"\n",
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"async def summarize_text(text: str, max_length: int = 100) -> Dict[str, str]:\n",
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" await asyncio.sleep(0.15)\n",
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" summary = text[:max_length] + \"...\" if len(text) > max_length else text\n",
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" return {\"original_length\": len(text), \"summary\": summary, \"compression_ratio\": round(len(summary) / len(text), 2)}\n",
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"\n",
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"async def search_knowledge(query: str, top_k: int = 3) -> List[Dict[str, Any]]:\n",
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" await asyncio.sleep(0.25)\n",
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" mock_results = [{\"title\": f\"Result {i+1} for '{query}'\", \"score\": round(random.uniform(0.5, 1.0), 2)} for i in range(top_k)]\n",
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" return sorted(mock_results, key=lambda x: x[\"score\"], reverse=True)\n"
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],
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"metadata": {
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"id": "Ojfde3C2bkNA"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"async def run_mcp_demo():\n",
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" print(\"=\" * 60)\n",
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" print(\"MODEL CONTEXT PROTOCOL (MCP) - ADVANCED TUTORIAL\")\n",
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" print(\"=\" * 60)\n",
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" print(\"\\n[1] Setting up MCP Server...\")\n",
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" server = MCPServer(\"knowledge-server\")\n",
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" print(\"\\n[2] Registering resources...\")\n",
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" server.register_resource(Resource(uri=\"docs://python-guide\", name=\"Python Programming Guide\", description=\"Comprehensive Python documentation\", mime_type=\"text/markdown\", content=\"# Python Guide\\nPython is a high-level programming language...\"))\n",
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" server.register_resource(Resource(uri=\"data://sales-2024\", name=\"2024 Sales Data\", description=\"Annual sales metrics\", mime_type=\"application/json\", content={\"q1\": 125000, \"q2\": 142000, \"q3\": 138000, \"q4\": 165000}))\n",
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" print(\"\\n[3] Registering tools...\")\n",
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" server.register_tool(Tool(name=\"analyze_sentiment\", description=\"Analyze sentiment of text\", parameters={\"text\": {\"type\": \"string\", \"required\": True}}, handler=analyze_sentiment))\n",
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" server.register_tool(Tool(name=\"summarize_text\", description=\"Summarize long text\", parameters={\"text\": {\"type\": \"string\", \"required\": True}, \"max_length\": {\"type\": \"integer\", \"default\": 100}}, handler=summarize_text))\n",
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" server.register_tool(Tool(name=\"search_knowledge\", description=\"Search knowledge base\", parameters={\"query\": {\"type\": \"string\", \"required\": True}, \"top_k\": {\"type\": \"integer\", \"default\": 3}}, handler=search_knowledge))\n",
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" client = MCPClient(\"demo-client\")\n",
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" client.connect_server(server)\n",
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" print(\"\\n\" + \"=\" * 60)\n",
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" print(\"DEMONSTRATION: MCP IN ACTION\")\n",
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" print(\"=\" * 60)\n",
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" print(\"\\n[Demo 1] Listing available resources...\")\n",
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" resources = await client.query_resources(\"knowledge-server\")\n",
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" for res in resources:\n",
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" print(f\" • {res['name']}: {res['description']}\")\n",
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" print(\"\\n[Demo 2] Fetching sales data resource...\")\n",
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" sales_resource = await client.fetch_resource(\"knowledge-server\", \"data://sales-2024\")\n",
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" if sales_resource:\n",
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" print(f\" Data: {json.dumps(sales_resource.content, indent=2)}\")\n",
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" print(\"\\n[Demo 3] Analyzing sentiment...\")\n",
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" sentiment_result = await client.call_tool(\"knowledge-server\", \"analyze_sentiment\", text=\"MCP is an amazing protocol for AI integration!\")\n",
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" print(f\" Result: {json.dumps(sentiment_result, indent=2)}\")\n",
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" print(\"\\n[Demo 4] Summarizing text...\")\n",
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" summary_result = await client.call_tool(\"knowledge-server\", \"summarize_text\", text=\"The Model Context Protocol enables seamless integration between AI models and external data sources...\", max_length=50)\n",
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" print(f\" Summary: {summary_result['summary']}\")\n",
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" print(\"\\n[Demo 5] Searching knowledge base...\")\n",
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" search_result = await client.call_tool(\"knowledge-server\", \"search_knowledge\", query=\"machine learning\", top_k=3)\n",
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" print(\" Top results:\")\n",
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" for result in search_result:\n",
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" print(f\" - {result['title']} (score: {result['score']})\")\n",
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" print(\"\\n[Demo 6] Current context window...\")\n",
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" context = client.get_context()\n",
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" print(f\" Context length: {len(context)} messages\")\n",
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" for i, msg in enumerate(context[-3:], 1):\n",
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" print(f\" {i}. [{msg['role']}] {msg['content']}\")\n",
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" print(\"\\n\" + \"=\" * 60)\n",
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" print(\"✓ MCP Tutorial Complete!\")\n",
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" print(\"=\" * 60)\n",
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" print(\"\\nKey Takeaways:\")\n",
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" print(\"• MCP enables modular AI-to-resource connections\")\n",
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" print(\"• Resources provide context from external sources\")\n",
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" print(\"• Tools enable dynamic operations and actions\")\n",
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" print(\"• Async design supports efficient I/O operations\")\n",
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"\n",
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"if __name__ == \"__main__\":\n",
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" import sys\n",
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" if 'ipykernel' in sys.modules or 'google.colab' in sys.modules:\n",
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" await run_mcp_demo()\n",
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" else:\n",
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" asyncio.run(run_mcp_demo())"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "fVoEV4Pgabnx",
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"outputId": "c66bf70e-ed22-4b09-ac75-cc4757cbeb8c"
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},
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"execution_count": 3,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"============================================================\n",
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"MODEL CONTEXT PROTOCOL (MCP) - ADVANCED TUTORIAL\n",
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"============================================================\n",
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"\n",
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"[1] Setting up MCP Server...\n",
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"✓ MCP Server 'knowledge-server' initialized with capabilities: ['resources', 'tools', 'prompts', 'logging']\n",
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"\n",
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"[2] Registering resources...\n",
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" → Resource registered: Python Programming Guide (docs://python-guide)\n",
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" → Resource registered: 2024 Sales Data (data://sales-2024)\n",
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"\n",
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"[3] Registering tools...\n",
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" → Tool registered: analyze_sentiment\n",
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" → Tool registered: summarize_text\n",
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" → Tool registered: search_knowledge\n",
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"\n",
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"✓ MCP Client 'demo-client' initialized\n",
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" → Connected to server: knowledge-server\n",
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"\n",
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"============================================================\n",
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"DEMONSTRATION: MCP IN ACTION\n",
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"============================================================\n",
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"\n",
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"[Demo 1] Listing available resources...\n",
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" • Python Programming Guide: Comprehensive Python documentation\n",
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" • 2024 Sales Data: Annual sales metrics\n",
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"\n",
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"[Demo 2] Fetching sales data resource...\n",
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" Data: {\n",
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" \"q1\": 125000,\n",
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" \"q2\": 142000,\n",
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" \"q3\": 138000,\n",
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" \"q4\": 165000\n",
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"}\n",
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"\n",
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"[Demo 3] Analyzing sentiment...\n",
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" Result: {\n",
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" \"text\": \"MCP is an amazing protocol for AI integration!\",\n",
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" \"sentiment\": \"neutral\",\n",
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" \"confidence\": 0.81\n",
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"}\n",
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"\n",
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"[Demo 4] Summarizing text...\n",
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" Summary: The Model Context Protocol enables seamless integr...\n",
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"\n",
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"[Demo 5] Searching knowledge base...\n",
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" Top results:\n",
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" - Result 1 for 'machine learning' (score: 0.85)\n",
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" - Result 3 for 'machine learning' (score: 0.77)\n",
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" - Result 2 for 'machine learning' (score: 0.59)\n",
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"\n",
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"[Demo 6] Current context window...\n",
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" Context length: 4 messages\n",
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" 1. [system] Tool 'analyze_sentiment' executed\n",
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" 2. [system] Tool 'summarize_text' executed\n",
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" 3. [system] Tool 'search_knowledge' executed\n",
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"\n",
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"============================================================\n",
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"✓ MCP Tutorial Complete!\n",
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"============================================================\n",
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"\n",
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"Key Takeaways:\n",
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"• MCP enables modular AI-to-resource connections\n",
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"• Resources provide context from external sources\n",
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"• Tools enable dynamic operations and actions\n",
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"• Async design supports efficient I/O operations\n"
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]
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}
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]
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}
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]
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}

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