{"id":191967,"date":"2025-04-19T02:51:02","date_gmt":"2025-04-19T02:51:02","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/how-to-create-an-mcp-client-server-using-langchain\/"},"modified":"2025-04-19T02:51:02","modified_gmt":"2025-04-19T02:51:02","slug":"how-to-create-an-mcp-client-server-using-langchain","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=191967","title":{"rendered":"How to Create an MCP Client Server Using LangChain"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div id=\"article-start\">\n<p>The world of AI and <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2023\/03\/an-introduction-to-large-language-models-llms\/\">Large Language Models (LLMs)<\/a> moves quickly. Integrating external tools and real-time data is vital for building truly powerful applications. The Model Context Protocol (MCP) offers a standard way to bridge this gap. This guide provides a clear, beginner-friendly walkthrough for creating an MCP client server using LangChain. Understanding the MCP client server architecture helps build robust AI agents. We\u2019ll cover the essentials, including what is MCP server functionality, and provide a practical MCP client server using LangChain example.<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-understanding-the-model-context-protocol-mcp\">Understanding the Model Context Protocol (MCP)<\/h2>\n<p>So, what is MCP server and client interaction all about? The Model Context Protocol (MCP) is an open-standard system. Anthropic developed it to connect LLMs with external tools and data sources effectively. It uses a structured and reusable approach. MCP helps AI models talk to different systems. This allows them to access current information and do tasks beyond their initial training. Think of it as a universal translator between the AI and the outside world, forming the core of the MCP client server architecture.<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-key-features-of-mcp\">Key Features of MCP<\/h3>\n<p>MCP stands out due to several important features:<\/p>\n<ol class=\"wp-block-list\">\n<li><strong>Standardized Integration: <\/strong>MCP gives a single, consistent way to connect LLMs to many tools and data sources. This removes the need for unique code for every connection. It simplifies the MCP client server using LangChain setup.<\/li>\n<li><strong>Context Management: <\/strong>The protocol ensures the AI model keeps track of the conversation context during multiple steps. This prevents losing important information when tasks require several interactions.<\/li>\n<li><strong>Security and Isolation: <\/strong>MCP includes strong security measures. It controls access strictly and keeps server connections separate using permission boundaries. This ensures safe communication between the client and server.<\/li>\n<\/ol>\n<h3 class=\"wp-block-heading\" id=\"h-role-of-mcp-in-llm-based-applications\">Role of MCP in LLM-Based Applications<\/h3>\n<p>LLM applications often need outside data. They might need to query databases, fetch documents, or use web APIs. MCP acts as a crucial middle layer. It lets models interact with these external resources smoothly, without needing manual steps. Using an MCP client server using LangChain lets developers build smarter AI agents. These agents become more capable, work faster, and operate securely within a well-defined MCP client server architecture. This setup is fundamental for advanced AI assistants. Now Let\u2019s look at the implementation part.<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-setting-up-the-environment\">Setting Up the Environment<\/h2>\n<p>Before building our MCP client server using LangChain, let\u2019s prepare the environment. You need these items:<\/p>\n<ul class=\"wp-block-list\">\n<li>Python version 3.11 or newer.<\/li>\n<li>Set up a new virtual environment (optional)<\/li>\n<li>An API key (e.g., OpenAI or Groq, depending on the model you choose).<\/li>\n<li>Specific Python libraries: <em>langchain-mcp-adapters<\/em>, <em>langgraph<\/em>, and an LLM library (like <em>langchain-openai<\/em> or <em>langchain-groq<\/em>) of your choice.<\/li>\n<\/ul>\n<p>Install the needed libraries using pip. Open your terminal or command prompt and run:<\/p>\n<pre class=\"wp-block-code\"><code>pip install langchain-mcp-adapters langgraph langchain-groq # Or langchain-openai<\/code><\/pre>\n<p>Make sure you have the correct Python version and necessary keys ready.<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-building-the-mcp-server\">Building the MCP Server<\/h2>\n<p>The MCP server\u2019s job is to offer tools the client can use. In our MCP client server using langchain example, we will build a simple server. This server will handle basic math operations as well as complex weather api to get weather details of a city. Understanding what is MCP server functionality starts here.<\/p>\n<p>Create a Python file named mcp_server.py:<\/p>\n<ol class=\"wp-block-list\">\n<li>Let\u2019s import the required libraries<\/li>\n<\/ol>\n<pre class=\"wp-block-code\"><code>import math\n\nimport requests\n\nfrom mcp.server.fastmcp import FastMCP<\/code><\/pre>\n<p>2. Initialize the FastMCP object<\/p>\n<pre class=\"wp-block-code\"><code>mcp= FastMCP(\"Math\")<\/code><\/pre>\n<p>3. Let\u2019s define the math tools<\/p>\n<pre class=\"wp-block-code\"><code>@mcp.tool()\n\ndef add(a: int, b: int) -&gt; int:\n\n\u00a0\u00a0\u00a0print(f\"Server received add request: {a}, {b}\")\n\n\u00a0\u00a0\u00a0return a + b\n\n@mcp.tool()\n\ndef multiply(a: int, b: int) -&gt; int:\n\n\u00a0\u00a0\u00a0print(f\"Server received multiply request: {a}, {b}\")\n\n\u00a0\u00a0\u00a0return a * b\n\n@mcp.tool()\n\ndef sine(a: int) -&gt; int:\n\n\u00a0\u00a0\u00a0print(f\"Server received sine request: {a}\")\n\n\u00a0\u00a0\u00a0return math.sin(a)<\/code><\/pre>\n<p>4. Now, Let\u2019s define a weather tool, make sure you have API from <a href=\"https:\/\/www.weatherapi.com\/\">here<\/a>.<\/p>\n<pre class=\"wp-block-code\"><code>WEATHER_API_KEY = \"YOUR_API_KEY\"\n\n@mcp.tool()\n\ndef get_weather(city: str) -&gt; dict:\n\n\u00a0\u00a0\u00a0\"\"\"\n\n\u00a0\u00a0\u00a0Fetch current weather for a given city using WeatherAPI.com.\n\n\u00a0\u00a0\u00a0Returns a dictionary with city, temperature (C), and condition.\n\n\u00a0\u00a0\u00a0\"\"\"\n\n\u00a0\u00a0\u00a0print(f\"Server received weather request: {city}\")\n\n\u00a0\u00a0\u00a0url = f\"http:\/\/api.weatherapi.com\/v1\/current.json?key={WEATHER_API_KEY}&amp;q={city}\"\n\n\u00a0\u00a0\u00a0response = requests.get(url)\n\n\u00a0\u00a0\u00a0if response.status_code != 200:\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0return {\"error\": f\"Failed to fetch weather for {city}.\"}\n\n\u00a0\u00a0\u00a0data = response.json()\n\n\u00a0\u00a0\u00a0return {\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"city\": data[\"location\"][\"name\"],\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"region\": data[\"location\"][\"region\"],\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"country\": data[\"location\"][\"country\"],\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"temperature_C\": data[\"current\"][\"temp_c\"],\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"condition\": data[\"current\"][\"condition\"][\"text\"]\n\n\u00a0\u00a0\u00a0}\n\n\u00a0\u00a0\u00a05. Now, instantiate the mcp server\u00a0\n\nif __name__ ==\"__main__\":\n\n\u00a0\u00a0\u00a0print(\"Starting MCP Server....\")\n\n\u00a0\u00a0\u00a0mcp.run(transport=\"stdio\")<\/code><\/pre>\n<p><strong>Explanation:<\/strong><\/p>\n<p>This script sets up a simple MCP server named \u201cMath\u201d. It uses FastMCP to define four tools, add, multiply, sine and <em>get_weather <\/em>marked by the <em>@mcp.tool()<\/em> decorator. Type hints tell MCP about the expected inputs and outputs. The server runs using standard input\/output (stdio) for communication when executed directly. This demonstrates what is MCP server in a basic setup.<\/p>\n<p><strong>Run the server:<\/strong> Open your terminal and navigate to the directory containing mcp_server.py. Then run:\u00a0<\/p>\n<pre class=\"wp-block-code\"><code>python mcp_server.py<\/code><\/pre>\n<p>The server should start without any warnings. This server will keep on running for the client to access the tools<\/p>\n<p><strong>Output:<\/strong><\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"638\" height=\"49\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/04\/mcp_server_output.webp\" alt=\"MCP client server using langchain\" class=\"wp-image-231495\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/04\/mcp_server_output.webp 638w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/04\/mcp_server_output-300x23.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/04\/mcp_server_output-150x12.webp 150w\" sizes=\"auto, (max-width: 638px) 100vw, 638px\"\/><\/figure>\n<\/div>\n<h2 class=\"wp-block-heading\" id=\"h-building-the-mcp-client\">Building the MCP Client<\/h2>\n<p>The client connects to the server, sends requests (like asking the agent to perform a calculation and fetch the live weather), and handles the responses. This demonstrates the client side of the MCP client server using LangChain.<\/p>\n<p>Create a Python file named client.py:<\/p>\n<ol class=\"wp-block-list\">\n<li>Import the necessary libraries first<\/li>\n<\/ol>\n<pre class=\"wp-block-code\"><code># client.py\n\nfrom mcp import ClientSession, StdioServerParameters\n\nfrom mcp.client.stdio import stdio_client\n\nfrom langchain_mcp_adapters.tools import load_mcp_tools\n\nfrom langgraph.prebuilt import create_react_agent\n\nfrom langchain_groq import ChatGroq\n\nfrom langchain_openai import ChatOpenAI\n\nimport asyncio\n\nimport os<\/code><\/pre>\n<ol start=\"2\" class=\"wp-block-list\">\n<li>Set up the API key for the LLM (Groq or OpenAI) and initialize the LLM model\u00a0<\/li>\n<\/ol>\n<pre class=\"wp-block-code\"><code># Set your API key (replace with your actual key or use environment variables)\n\nGROQ_API_KEY = \"YOUR_GROQ_API_KEY\" # Replace with your key\n\nos.environ[\"GROQ_API_KEY\"] = GROQ_API_KEY\n\n# OPENAI_API_KEY = \"YOUR_OPENAI_API_KEY\"\n\n# os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n\n# Initialize the LLM model\n\nmodel = ChatGroq(model=\"llama3-8b-8192\", temperature=0)\n\n# model = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)<\/code><\/pre>\n<ol start=\"3\" class=\"wp-block-list\">\n<li>Now, define the parameters to start the MCP server process.<\/li>\n<\/ol>\n<pre class=\"wp-block-code\"><code>server_params = StdioServerParameters(\n\n\u00a0\u00a0\u00a0command=\"python\",\u00a0 \u00a0 \u00a0 # Command to execute\n\n\u00a0\u00a0\u00a0args=[\"mcp_server.py\"] # Arguments for the command (our server script)\n\n)<\/code><\/pre>\n<ol start=\"4\" class=\"wp-block-list\">\n<li>Let\u2019s define the Asynchronous function to run the agent interaction\u00a0<\/li>\n<\/ol>\n<pre class=\"wp-block-code\"><code>async def run_agent():\n\n\u00a0\u00a0\u00a0async with stdio_client(server_params) as (read, write):\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0async with ClientSession(read, write) as session:\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0await session.initialize()\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0print(\"MCP Session Initialized.\")\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0tools = await load_mcp_tools(session)\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0print(f\"Loaded Tools: {[tool.name for tool in tools]}\")\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0agent = create_react_agent(model, tools)\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0print(\"ReAct Agent Created.\")\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0print(f\"Invoking agent with query\")\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0response = await agent.ainvoke({\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"messages\": [(\"user\", \"What is (7+9)x17, then give me sine of the output recieved and then tell me What's the weather in Torronto, Canada?\")]\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0})\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0print(\"Agent invocation complete.\")\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0# Return the content of the last message (usually the agent's final answer)\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0return response[\"messages\"][-1].content<\/code><\/pre>\n<ol start=\"5\" class=\"wp-block-list\">\n<li>Now, run this function and wait for the results on th terminal\u00a0<\/li>\n<\/ol>\n<pre class=\"wp-block-code\"><code># Standard Python entry point check\n\nif __name__ == \"__main__\":\n\n\u00a0\u00a0\u00a0# Run the asynchronous run_agent function and wait for the result\n\n\u00a0\u00a0\u00a0print(\"Starting MCP Client...\")\n\n\u00a0\u00a0\u00a0result = asyncio.run(run_agent())\n\n\u00a0\u00a0\u00a0print(\"\\nAgent Final Response:\")\n\n\u00a0\u00a0\u00a0print(result)<\/code><\/pre>\n<p><strong>Explanation:<\/strong><\/p>\n<p>This client script configures an LLM (using ChatGroq here; remember to set your API key). It defines how to start the server using StdioServerParameters. The run_agent function connects to the server via stdio_client, creates a ClientSession, and initializes it. load_mcp_tools fetches the server\u2019s tools for LangChain. A create_react_agent uses the LLM and tools to process a user query. Finally, agent.ainvoke sends the query, letting the agent potentially use the server\u2019s tools to find the answer. This shows a complete MCP client server using langchain example.<\/p>\n<p><strong>Run the client:<\/strong><\/p>\n<pre class=\"wp-block-code\"><code>python client.py<\/code><\/pre>\n<p><strong>Output:<\/strong><\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"655\" height=\"275\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/04\/mcp_client_output-1.webp\" alt=\"MCP client server using langchain\" class=\"wp-image-231496\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/04\/mcp_client_output-1.webp 655w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/04\/mcp_client_output-1-300x126.webp 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2025\/04\/mcp_client_output-1-150x63.webp 150w\" sizes=\"auto, (max-width: 655px) 100vw, 655px\"\/><\/figure>\n<\/div>\n<p>We can see that the client starts the server process, initializes the connection, loads tools, invokes the agent, and prints the final answer calculated by calling the server\u2019s add tool also called weather api and retrieving the live weather data.<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-real-world-applications\">Real-World Applications<\/h2>\n<p>Using an MCP client server using LangChain opens up many possibilities for creating sophisticated AI agents. Some practical applications include:<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>LLM Independency:<\/strong> By utilizing Langchain, we can now integrate any LLM with MCP. Previously we were<\/li>\n<li><strong>Data Retrieval: <\/strong>Agents can connect to database servers via MCP to fetch real-time customer data or query internal knowledge bases.<\/li>\n<li><strong>Document Processing: <\/strong>An agent could use MCP tools to interact with a document management system, allowing it to summarize, extract information, or update documents based on user requests.<\/li>\n<li><strong>Task Automation:<\/strong> Integrate with various business systems (like CRMs, calendars, or project management tools) through MCP servers to automate routine tasks like scheduling meetings or updating sales records. The MCP client server architecture supports these complex workflows.<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-best-practices\">Best Practices<\/h2>\n<p>When building your MCP client server using LangChain, follow good practices for better results:<\/p>\n<ul class=\"wp-block-list\">\n<li>Adopt a modular design by creating specific tools for distinct tasks and keeping server logic separate from client logic.<\/li>\n<li>Implement robust error handling in both server tools and the client agent so the system can manage failures gracefully. <\/li>\n<li>Prioritize security, especially if the server handles sensitive data, by using MCP\u2019s features like access controls and permission boundaries. <\/li>\n<li>Provide clear descriptions and docstrings for your MCP tools; this helps the agent understand their purpose and usage.<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\" id=\"h-common-pitfalls\">Common Pitfalls<\/h2>\n<p>Be mindful of potential issues when developing your system. Context loss can occur in complex conversations if the agent framework doesn\u2019t manage state properly, leading to errors. Poor resource management in long-running MCP servers might cause memory leaks or performance degradation, so handle connections and file handles carefully. Ensure compatibility between the client and server transport mechanisms, as mismatches (like one using stdio and the other expecting HTTP) will prevent communication. Finally, watch for tool schema mismatches where the server tool\u2019s definition doesn\u2019t align with the client\u2019s expectation, which can block tool execution. Addressing these points strengthens your MCP client server using LangChain implementation.<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-conclusion\">Conclusion<\/h2>\n<p>Leveraging the Model Context Protocol with LangChain provides a powerful and standardized way to build advanced AI agents. By creating an MCP client server using LangChain, you enable your LLMs to interact securely and effectively with external tools and data sources. This guide demonstrated a basic MCP client server using LangChain example, outlining the core MCP client server architecture and what is MCP server functionality entails. This approach simplifies integration, boosts agent capabilities, and ensures reliable operations, paving the way for more intelligent and useful AI applications.<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-frequently-asked-questions\">Frequently Asked Questions<\/h2>\n<div class=\"schema-faq wp-block-yoast-faq-block\">\n<div class=\"schema-faq-section\" id=\"faq-question-1744885686700\"><strong class=\"schema-faq-question\">Q1. What is the Model Context Protocol (MCP)?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. MCP is an open standard designed by Anthropic. It provides a structured way for Large Language Models (LLMs) to interact with external tools and data sources securely.<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1744885699514\"><strong class=\"schema-faq-question\">Q2. Why use MCP with LangChain for client-server interactions?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. LangChain provides the framework for building agents, while MCP offers a standardized protocol for tool communication. Combining them simplifies building agents that can reliably use external capabilities.<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1744885711937\"><strong class=\"schema-faq-question\">Q3. What communication methods (transports) does MCP support?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. MCP is designed to be transport-agnostic. Common implementations use standard input\/output (stdio) for local processes or HTTP-based Server-Sent Events (SSE) for network communication.<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1744885729419\"><strong class=\"schema-faq-question\">Q4. Is the MCP client server architecture secure?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. Yes, MCP is designed with security in mind. It includes features like permission boundaries and connection isolation to ensure secure interactions between clients and servers.<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1744885736035\"><strong class=\"schema-faq-question\">Q5. Can I use MCP with LLMs other than Groq or OpenAI models?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. Absolutely. LangChain supports many LLM providers. As long as the chosen LLM works with LangChain\/LangGraph agent frameworks, it can interact with tools loaded via an MCP client.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"border-top py-3 author-info my-4\">\n<div class=\"author-card d-flex align-items-center\">\n<div class=\"flex-shrink-0 overflow-hidden\">\n                                    <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/author\/harsh9480979\/\" class=\"text-decoration-none active-avatar\"><br \/>\n                                                                       <img decoding=\"async\" src=\"https:\/\/av-eks-lekhak.s3.amazonaws.com\/media\/lekhak-profile-images\/converted_image_0fBqNLi.webp\" width=\"48\" height=\"48\" alt=\"Harsh Mishra\" loading=\"lazy\" class=\"rounded-circle\"\/><\/p>\n<p>                                <\/a>\n                                <\/div>\n<\/p><\/div>\n<p>Harsh Mishra is an AI\/ML Engineer who spends more time talking to Large Language Models than actual humans. Passionate about GenAI, NLP, and making machines smarter (so they don\u2019t replace him just yet). When not optimizing models, he\u2019s probably optimizing his coffee intake. \ud83d\ude80\u2615<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<p><h4 class=\"fs-24 text-dark\">Login to continue reading and enjoy expert-curated content.<\/h4>\n<p>                        <button class=\"btn btn-primary mx-auto d-table\" data-bs-toggle=\"modal\" data-bs-target=\"#loginModal\" id=\"readMoreBtn\">Keep Reading for Free<\/button>\n                    <\/p>\n\n","protected":false},"excerpt":{"rendered":"<p>The world of AI and Large Language Models (LLMs) moves quickly. Integrating external tools and real-time data is vital for building truly powerful applications. The Model Context Protocol (MCP) offers a standard way to bridge this gap. This guide provides a clear, beginner-friendly walkthrough for creating an MCP client server using LangChain. 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