Unlocking LLM Potential: A Developer's Guide to Anthropic's Model Context Protocol (MCP)

Large Language Models (LLMs) like Claude, Gemini, and GPT-4 have shown incredible reasoning capabilities. But even the smartest LLM is often hampered by its isolation – trapped behind information silos and unable to interact smoothly with the external tools and data sources needed for complex, real-world tasks. Integrating each new data source or tool often requires custom, brittle connectors, creating a significant development bottleneck.

Enter the Model Context Protocol (MCP), an open standard introduced by Anthropic in late 2024. MCP aims to fundamentally change how AI assistants connect to the systems where data lives and actions happen. Think of it like USB-C for AI applications: a universal, standardized way to connect models to diverse data sources, tools, and environments, replacing fragmented, one-off integrations with a single, reliable protocol.

This post dives deep into MCP, exploring what it is, why it represents a significant step forward for developers building on LLMs, how it compares to existing approaches like function calling, and illustrating its core principles with a practical example using the Gemini API and Yahoo Finance data.

What Exactly is the Model Context Protocol (MCP)?

At its core, MCP is an open protocol specification designed to standardize secure, two-way communication between AI-powered applications (like LLM clients) and external capabilities (like data sources or tools). Its primary goal is to help LLMs produce better, more relevant responses by giving them structured access to the context they need.

The MCP Architecture: Hosts, Clients, and Servers

MCP employs a standard client-server architecture involving three main components:

1.   MCP Host: This is the AI-powered application or environment the end-user interacts with. Examples include the Claude Desktop app, an IDE plugin (like VS Code with GitHub Copilot agent mode), or any custom LLM application. The Host manages connections to one or more MCP Servers.

2.   MCP Client: An intermediary component managed by the Host. Each MCP Client maintains a secure, sandboxed, one-to-one connection with a specific MCP Server.² The Host spawns a client for each server connection needed.

3.   MCP Server: A lightweight program that implements the MCP standard and exposes specific capabilities from external systems.¹ These capabilities could involve accessing local files, querying databases (like Postgres), interacting with remote APIs (like Slack or Google Drive), or controlling tools (like a web browser via Puppeteer).

This architecture allows a single Host application to securely connect to and utilize capabilities from multiple, diverse MCP Servers simultaneously.

Communication and Primitives: The Language of MCP

MCP uses JSON-RPC 2.0 as its underlying communication protocol.⁵ This choice makes MCP language-agnostic and relatively easy to implement, leveraging a well-understood standard with existing libraries across many programming languages. This facilitates broad adoption, aligning with the goal of creating a universal standard rather than a niche, potentially higher-performance but less accessible custom protocol.

Communication revolves around a set of defined message types called primitives, which represent the core building blocks for interaction:

Server-Exposed Primitives (Providing Context/Capabilities to the LLM):

• Tools: Executable functions or actions that the LLM (via the Host/Client) can request the Server to perform. Examples include fetching data from an API, writing to a database, or sending a message. Each tool has a name, description, and a schema defining its input/output parameters. This is analogous to functions in traditional function calling.
• Resources: Structured data or content that the Server makes available to the LLM for context. This could be file contents, database records, code snippets, or API documentation. Unlike tools, resources are typically passive information used to enrich the LLM’s understanding.
• Prompts: Pre-defined prompt templates or workflow instructions provided by the Server. These can guide the LLM through common or complex tasks associated with the server’s capabilities, potentially involving sequences of tool calls or resource fetches. They represent reusable recipes for interaction.

Client-Exposed Primitives (Enabling Server Interaction):

• Roots: URIs (often file paths or URLs) communicated by the Client to the Server, indicating the relevant operational boundaries or workspace context. This helps the server focus its operations, for example, on a specific project directory.
• Sampling: Allows an MCP Server to request the Host’s LLM to generate text (a "completion") based on a prompt provided by the Server. This enables more complex, multi-step reasoning and agentic behavior where the server might need the LLM’s help during its own execution flow. Anthropic advises that human oversight should generally be involved in approving sampling requests.

The explicit separation of Tools, Resources, and Prompts is a notable design choice. It allows servers to convey richer semantic information about their capabilities and the intended use of context, moving beyond simple function execution. This structured approach potentially enables more sophisticated and reliable agent behavior, as the AI can understand not just what it can call (Tools), but also the relevant background information (Resources) and pre-defined workflows (Prompts).

The MCP Ecosystem: SDKs, Servers, and Community

Anthropic is actively fostering an ecosystem around MCP:

• Specification & SDKs: The core MCP specification is open-source. Anthropic and partners provide official SDKs in multiple languages (TypeScript, Python, Java, Kotlin, C#, Swift, Rust) to simplify building MCP clients and servers.
• Open-Source Servers: A growing repository of pre-built MCP servers for popular tools like Google Drive, Slack, GitHub, Git, Postgres, Puppeteer, and Chroma is available. This provides immediate value and examples for developers.
• Tooling: An MCP Inspector tool helps developers test and debug their server implementations.
• Community & Collaboration: MCP is positioned as a collaborative, open-source project inviting community contributions.¹ Early adopters include companies like Block and Apollo, with tool vendors like Zed, Replit, Codeium, Sourcegraph, Microsoft, and Apify also integrating or partnering with MCP.

This strategy of providing not just a specification but also SDKs, pre-built components, and fostering partnerships is crucial for accelerating adoption and aiming to establish MCP as a de facto standard.¹

Why MCP is a Big Deal for Developers and the AI Ecosystem

MCP isn’t just another API; it’s a potential paradigm shift in how AI applications are built and interact with the world. Its significance stems from addressing several key challenges in the current LLM landscape.

Taming Integration Complexity: The M×N Problem Solved?

The most immediate benefit is simplifying integration. Currently, connecting M different LLM applications to N different tools or data sources often requires developing and maintaining M × N custom connectors – a combinatorial explosion of effort.

MCP proposes transforming this into an M + N problem. AI applications (Hosts) implement an MCP client once, and tool/data providers implement an MCP server once. Any MCP-compliant client can then theoretically talk to any MCP-compliant server. This drastically reduces the integration burden, freeing up developer time and resources.

Fostering Interoperability and Standardization

By establishing a common protocol, MCP promotes interoperability. Developers gain the flexibility to potentially swap LLM providers or underlying tools more easily if they all adhere to the MCP standard, reducing vendor lock-in at the integration layer.

Furthermore, standardization introduces common design patterns, making it easier for developers to onboard and understand how different AI services interact. This shared understanding becomes increasingly important with the rise of complex, multi-component agentic systems.

Enhancing LLM Capabilities and Efficiency

MCP provides a structured channel for LLMs to access diverse, often real-time, external context (via Resources) and capabilities (via Tools). This leads to more relevant, accurate, and useful LLM responses compared to models operating solely on their internal knowledge or limited context windows.

The protocol also enables two-way interaction, allowing LLMs not just to read data but also to trigger actions and write back to external systems through MCP servers.¹ Standardized context management might also improve efficiency by minimizing the need to stuff vast amounts of unstructured context into prompts.

Powering the Next Generation of AI Agents

MCP is seen as a critical enabler for more sophisticated agentic AI. Agents often need to autonomously interact with multiple tools and data sources to accomplish complex tasks. MCP provides the standardized "connective tissue" for these interactions. It allows context to be maintained as an agent potentially moves between different tools or datasets connected via MCP. Primitives like Sampling directly support multi-step reasoning workflows essential for agents.

By lowering the integration barrier and providing an ecosystem of connectable tools, MCP could significantly democratize the development of complex AI agents, making them more feasible for smaller teams and individual developers beyond large tech organizations.

Security and Control Considerations

MCP incorporates security considerations in its design. The Host application controls which MCP Servers its Clients are allowed to connect to, giving users or organizations granular control over what data and tools an AI assistant can access.² The client-per-server model also provides a degree of sandboxing. MCP aims to provide standardized governance over how context is stored and shared.

However, it’s crucial to note that MCP itself does not handle authentication. The responsibility for securing the MCP server endpoint and authenticating incoming client requests lies with the developer implementing the server.

Adoption and Potential Challenges

The ultimate success of MCP hinges on widespread adoption. For the "USB-C for AI" vision to materialize, both AI application developers (clients) and tool/service providers (servers) need to embrace the standard. While early adoption and partnerships are promising, MCP faces competition from evolving vendor-specific frameworks (like OpenAI’s Agents SDK) and potentially other emerging standards. Developer inertia or a preference for tightly integrated vendor solutions could also slow adoption. Furthermore, while standardization brings benefits, there’s always a risk that a rigid standard could inadvertently stifle innovation in how AI interacts with highly complex or novel tools that don’t fit neatly into the defined primitives.

MCP vs. Function Calling (Gemini, OpenAI) & Other Tooling

It’s important to understand how MCP relates to, and differs from, the "function calling" or "tool use" features offered by LLM providers like Google (Gemini) and OpenAI.

• Function Calling (FC): This is primarily an LLM capability. It refers to the model’s ability to understand a user’s natural language request, determine that an external tool or function should be invoked, and generate a structured output (usually JSON) specifying which function to call and what parameters to use. The LLM generates the request to call the function; it doesn’t execute it. FC implementations are often specific to the LLM provider, although the underlying concepts and schema requirements (often based on OpenAPI subsets) can be similar.
• Model Context Protocol (MCP): This is a protocol standard defining the communication channel and interaction patterns between an AI application (Host/Client) and external capabilities (Servers). It standardizes how the connection is made and how information (including tool calls, data resources, and prompts) is exchanged using JSON-RPC. MCP aims to be model-agnostic.

Relationship: Function calling and MCP are often complementary. An LLM within an MCP Host application might use its native function calling capability to decide which MCP Tool primitive to invoke. The Host then uses the MCP client-server mechanism to execute that call via the standardized protocol. MCP provides the "pipes," while function calling is one way the LLM decides what goes through those pipes.

Key Differences Summarized:

Primary Purpose

• Model Context Protocol (MCP): A protocol standard for managing client-server communication.
• Gemini/OpenAI Function Calling: A feature where the LLM generates structured requests to trigger external functions.

Scope

• MCP: Standardizes how clients and servers connect and interact.
• Function Calling: Allows the LLM to create structured function/tool calls based on user input.

Vendor Neutrality

• MCP: Designed to be model- and vendor-agnostic, meaning it can work across different platforms.
• Function Calling: Typically tied to a specific vendor’s LLM, like OpenAI or Gemini.

Key Concepts

• MCP: Focuses on tools, resources, prompts, sampling, and root concepts.
• Function Calling: Centers around functions, tools, and their parameters.

Communication Model

• MCP: Uses a standardized client-server model, often based on JSON-RPC for structured communication.
• Function Calling: Works through an API request and response cycle between the app and the LLM.

Statefulness

• MCP: Supports persistent client-server connections, allowing it to maintain state across interactions.
• Function Calling: Mostly stateless — the application is responsible for managing context and state between calls.

Alternative Perspectives: Some argue that existing standards like OpenAPI, when combined with robust JSON schema definitions and capable LLMs, could achieve similar goals to MCP without needing a new protocol.¹⁸ The value proposition of MCP lies in its specific focus on AI interactions, its inclusion of primitives beyond simple tool execution (Resources, Prompts), and its goal of creating a unified, discoverable ecosystem built around a dedicated standard.⁸

Frameworks like LangChain/LlamaIndex: These operate at a higher level, often providing abstractions over different function calling implementations.¹⁸ They could potentially integrate with MCP by acting as an MCP Host or by wrapping MCP client/server interactions within their agent frameworks.

Illustrating the Principles: Gemini Function Calling + Yahoo Finance

To make the concept of connecting an LLM to an external tool concrete, let’s walk through an example.

Disclaimer: This example uses Google Gemini’s native Function Calling feature and the yfinance Python library. It does not implement MCP directly. Instead, it demonstrates the core principle that MCP aims to standardize: enabling an LLM to leverage an external tool (fetching stock data) to answer a user query that requires real-time, external information.

Goal: Allow a user to ask the Gemini model for the current stock price of a specific company (e.g., "What’s the current price of MSFT?") and have the model use a custom tool to fetch and return that price.

Components:

• LLM: Google Gemini Pro (via the google-generativeai Python SDK).
• External Tool Logic: A Python function using yfinance to get stock data.
• Connection Mechanism: Gemini API’s Function Calling feature.

Step 1: Setup and Dependencies

First, install the necessary Python libraries:

Bash

pip install google-generativeai yfinance

Then, configure your Gemini API key according to the official documentation.

Step 2: Define the Python Tool Function

Create a Python function that takes a ticker symbol and returns the current stock price using yfinance. Note that yfinance uses unofficial methods to access Yahoo Finance data.

Python

import yfinance as yf
import logging

def getcurrentstockprice(tickersymbol: str) -> float | str:
    """
    Gets the current stock price for a given ticker symbol using yfinance.
    Returns the price as a float or an error message string.
    """
    logging.info(f"Attempting to fetch stock price for: {ticker_symbol}")
    try:
        stock = yf.Ticker(ticker_symbol)
        # Get the most recent closing price available (often previous day’s close for ’1d’)
        # For more ’real-time’, consider shorter periods or different yfinance methods if available/reliable
        todays_data = stock.history(period=’1d’)
        if todays_data.empty:
             # Fallback: try to get current price info directly if history is empty
             info = stock.info
             current_price = info.get(’currentPrice’) or info.get(’regularMarketPrice’)
             if current_price:
                 logging.info(f"Fetched current price for {tickersymbol}: {currentprice}")
                 return float(current_price)
             else:
                 logging.warning(f"Could not find current price for {ticker_symbol} via info.")
                 return f"Error: Could not retrieve current price data for {ticker_symbol}."

        currentprice = todaysdata[’Close’].iloc[-1]
        logging.info(f"Fetched stock price for {tickersymbol}: {currentprice}")
        return float(current_price)
    except Exception as e:
        logging.error(f"Error fetching data for {ticker_symbol}: {e}")
        return f"Error: Could not retrieve data for ticker symbol {ticker_symbol}. Please ensure it’s a valid symbol."
Configure logging

logging.basicConfig(level=logging.INFO)
Example usage (optional - for testing the function directly)

print(getcurrentstock_price("AAPL"))

print(getcurrentstock_price("INVALIDTICKER"))

Code based on examples in

Step 3: Declare the Tool for Gemini

Now, describe this function to the Gemini model using its required format (based on OpenAPI schema).

Python

import google.generativeai as genai
from google.generativeai.types import HarmCategory, HarmBlockThreshold
--- Configure API Key ---

import os

GOOGLEAPIKEY=os.getenv(’GOOGLEAPIKEY’)

genai.configure(apikey=GOOGLEAPI_KEY)

Or configure directly:

genai.configure(apikey="YOURAPI_KEY")

--- ---

Function Declaration for Gemini

getstockprice_declaration = genai.protos.FunctionDeclaration(
    name=’getcurrentstock_price’,
    description=’Gets the current stock price for a given stock ticker symbol.’,
    parameters=genai.protos.Schema(
        type=genai.protos.Type.OBJECT,
        properties={
            ’ticker_symbol’: genai.protos.Schema(
                type=genai.protos.Type.STRING,
                description=’The stock ticker symbol (e.g., "AAPL", "GOOGL").’
            )
        },
        required=[’ticker_symbol’]
    )
)
Define the Tool for the model

stock_tool = genai.protos.Tool(
    functiondeclarations=[getstockpricedeclaration]
)
Map function name to the actual Python function

available_functions = {
    "getcurrentstockprice": getcurrentstockprice,
}

Code structure based on

Step 4: The Interaction Flow

Let’s orchestrate the conversation with the Gemini model.

Python
Initialize the Generative Model with the tool

Note: Adjust model name as needed (e.g., ’gemini-1.5-flash’)

model = genai.GenerativeModel(
    model_name=’gemini-1.5-pro-latest’, # Or another suitable model supporting function calling
    tools=[stock_tool],
    # Safety settings can be adjusted if needed
    safety_settings={
        HarmCategory.HARMCATEGORYHARASSMENT: HarmBlockThreshold.BLOCKMEDIUMAND_ABOVE,
        HarmCategory.HARMCATEGORYHATESPEECH: HarmBlockThreshold.BLOCKMEDIUMANDABOVE,
        HarmCategory.HARMCATEGORYSEXUALLYEXPLICIT: HarmBlockThreshold.BLOCKMEDIUMANDABOVE,
        HarmCategory.HARMCATEGORYDANGEROUSCONTENT: HarmBlockThreshold.BLOCKMEDIUMANDABOVE,
    }
)
Start a chat session (keeps history)

chat = model.startchat(enableautomaticfunctioncalling=True) # SDK handles calls automatically
--- User Interaction ---

prompt = "What is the current stock price for Microsoft (MSFT)?"
print(f"User: {prompt}")
Send prompt to the model

response = chat.send_message(prompt)
The SDK’s automatic function calling handles the intermediate steps:

1. Model responds with a FunctionCall request.

2. SDK identifies the function (’getcurrentstock_price’).

3. SDK calls the corresponding Python function (getcurrentstock_price("MSFT")).

4. SDK sends the function’s return value back to the model as a FunctionResponse.

5. Model generates the final text response based on the function result.

Print the final response from the model

print(f"Gemini: {response.text}")
--- Another Interaction (Example with potential ambiguity or error) ---

prompt_ambiguous = "How about Google’s stock?"
print(f"\nUser: {prompt_ambiguous}")
responseambiguous = chat.sendmessage(prompt_ambiguous)
print(f"Gemini: {response_ambiguous.text}") # Model might ask for clarification or use GOOGL/GOOG

prompt_invalid = "What’s the price for XYZINVALID?"
print(f"\nUser: {prompt_invalid}")
responseinvalid = chat.sendmessage(prompt_invalid)
print(f"Gemini: {response_invalid.text}") # Model should relay the error message from our function

Interaction flow based on

How this relates to MCP:

While this example uses Gemini’s specific function calling mechanism, MCP aims to standardize this entire interaction loop:

1.   Tool Definition: The getcurrentstockprice function logic would reside within an MCP Server, exposed via a standardized Tool primitive definition.

2.   Declaration/Discovery: An MCP Host (like our script, or potentially the Gemini model environment itself if it were an MCP client) would discover this tool via the MCP protocol, rather than needing a Gemini-specific declaration format.

3.   Communication: The request to call the tool and the response containing the price would travel via JSON-RPC over the MCP client-server connection.

The value MCP seeks to add is abstracting away the specifics of how the tool is declared and how the communication happens, providing a consistent interface regardless of the LLM used (if the LLM environment supports being an MCP Host/Client) or the tool being called (if the tool exposes an MCP Server). This example highlights the distinct steps involved – tool logic, declaration, invocation, execution, response handling – much of which MCP aims to streamline through standardization.

It also underscores that the reliability of the connection mechanism (Function Calling or MCP) is separate from the reliability of the underlying tool (yfinance accessing Yahoo Finance data). MCP standardizes the integration, but robust, well-maintained tools/servers are still essential.

Conclusion: Towards a More Connected AI Future

Anthropic’s Model Context Protocol represents a significant and ambitious effort to solve a fundamental challenge in applied AI: bridging the gap between powerful language models and the vast landscape of external data and tools. By proposing an open, standardized protocol – the "USB-C for AI" – MCP aims to replace the current patchwork of custom integrations with a more scalable, interoperable, and developer-friendly approach.

The potential benefits are substantial: reduced development complexity, enhanced LLM capabilities through richer context, greater flexibility in choosing models and tools, and a crucial foundation for building the next generation of sophisticated AI agents.

However, MCP is still relatively new, released in late 2024. Its success will depend on broad adoption across the ecosystem, including LLM providers, tool developers, and enterprise applications.¹⁰ While initially focused on developer and enterprise use cases, the long-term vision points towards a future where AI assistants can seamlessly leverage a vast network of interconnected capabilities via a standard protocol.

Getting Started with MCP:

For developers interested in exploring MCP further, here are the key resources:

• Official MCP Website & Documentation: modelcontextprotocol.io - The primary source for documentation, guides, and concepts.
• GitHub Organization: github.com/modelcontextprotocol - Houses the specification, SDKs (Python, TypeScript, C#, Java, etc.), pre-built server examples (Slack, GitHub, Postgres, etc.), and the MCP Inspector tool.
• Quickstart Guides: Available on the MCP website for building servers, building clients, and using MCP with Claude Desktop.
• Anthropic Resources: Check Anthropic’s blog and news releases for updates.

Whether MCP becomes the universal standard remains to be seen, but it represents a compelling vision for a more connected, capable, and collaborative AI future. Developers building applications that require deep integration with external systems should keep a close eye on MCP’s evolution and consider exploring its potential.