MCP: The Universal Connector for Building Smarter, Modular AI Agents

Introduction

AI agents, powered by large language models (LLMs), have the potential to revolutionize how we interact with information and automate complex tasks. However, to be truly useful, they must effectively leverage external context and data sources, utilize specialized tools, and generate and execute code. While AI agents are capable of tool usage, integrating these external components and making AI agents work with these tools has been a significant hurdle, often requiring bespoke, framework-specific solutions. This leads to fragmented ecosystems, duplicated effort, and systems that are difficult to maintain and scale.

Enter Model Context Protocol (MCP). Launched in late 2024 by Anthropic, MCP is rapidly emerging as a “USB-C for AI” – an open, universal standard designed to seamlessly connect AI agents with the tools and data they need. This article dives into what MCP is, how it empowers agent development, and how it is being adopted in leading open-source frameworks. We discuss the key capabilities MCP unlocks and its real-world applications. For practitioners, engineers, and researchers, understanding MCP is becoming increasingly relevant for building the next generation of powerful, context-aware, and modular AI systems.

Understanding the Model Context Protocol

What is MCP? – Core Concepts and Components

At its core, MCP is an open standard that defines a universal client-server protocol built on JSON-RPC 2.0 for how AI agents (Hosts/Clients) interact with external capabilities (Servers). MCP connections are typically stateful and use transport protocols such as STDIO for local connections and HTTP + SSE (Server-Sent Events) or Streamable HTTP for web-based connections.

The key components of MCP are:

• Host: The Host is the user-facing AI application, typically an LLM like ChatGPT or Claude, an IDE like Cursor, or custom agents built using LangChain.
• Client: The Client is a component within the host that manages communication with the MCP server.
• Server: The Server is a lightweight component that exposes external capabilities or data sources to the Host via the MCP protocol.

Instead of writing custom code for every new API, database, or service, an agent connects to an MCP-compatible Server. This Server exposes a set of functionalities – Tools, Resources, Prompts, and Sampling – through a standardized interface.

• Tools: These are executable functions or actions the AI agent can invoke, such as searching a file system, querying a database, sending an email, or calling a specialized API.
• Resources: These represent data sources that the agent can read from, like a document store or a vector database.
• Prompts: These are reusable prompt templates or instructions that guide the agent on how to use the available tools or approach specific tasks effectively.
• Sampling: These are requests from the Servers to the Host to perform specific actions or computations, such as chain-of-thought reasoning or code review, which enable complex interactions between the Host and Server.

Figure 1. Main Components of MCP

This standardization aims to enable significant interoperability. An AI agent developed using frameworks such as LangChain, CrewAI, or AutoGen, if designed to be MCP-compliant, could theoretically connect to any MCP Server. This connection would allow the agent to access the server’s exposed functionalities, irrespective of the specific Large Language Model (LLM) it employs (e.g., Claude, GPT-4, Llama) or the server’s internal implementation. From the MCP Server’s perspective, the protocol is designed so that the identity of the calling agent or model is not a prerequisite for interaction.

The Benefits of a Standardized Protocol

1. From M×N Fragmentation to M+N Modularity: Before MCP, integrating M different AI models or agent frameworks with N different tools often meant M×N custom integration efforts. Each framework had its own way of defining and incorporating tools. MCP transforms this into an M+N problem. An MCP server developed for a tool can be used by any of M agents. Similarly, an agent framework only needs to implement MCP client capabilities once to access N tools. This reduces integration overhead, resulting in a more scalable ecosystem.
2. Improved Interoperability: MCP allows different, disparate applications and agents to connect to a variety of servers. The Host could be an LLM, an IDE like Cursor or Windsurf, a python application built with LangChain. The Server could expose access to a database, make calls to GitHub, execute code or serve as external memory for the Host.
3. Future-Proofing and Decoupling: As LLMs and agent frameworks rapidly evolve, MCP provides a stable integration layer. You can swap out the LLM, upgrade your agent framework, or even migrate to a new one with greater confidence that your existing tool integrations will remain functional as long as they adhere to the MCP standard. The agent logic is decoupled from the tool implementation.
4. Democratizing Tool Development: MCP empowers developers to create and share specialized tool servers and focus on their area of expertise. A community-driven library of MCP connectors is emerging for popular services (e.g., Google Drive, GitHub, Google Maps, Slack) and niche internal systems.

MCP isn’t just a technical specification; it’s an architectural shift. It moves the integration burden from individual agent developers to a standardized protocol, allowing agent frameworks to focus on higher-level orchestration and reasoning while the “how” of tool interaction is handled by MCP.

MCP Implementation: Examples and Framework Integration

The practical value of MCP is best illustrated through its application in real-world scenarios and its integration into popular agent development frameworks.

Case Study 1: Block’s “Goose” Enterprise AI Agent

Financial technology company Block (formerly Square) developed an AI assistant named “Goose” to empower employees with coding assistance and data querying. Goose supports Anthropic’s Claude and OpenAI models and has integration to MCP servers powered by Databricks, Snowflake, GitHub, Jira, Slack and Google Drive.

• SQL Generation & Execution: Goose can generate SQL queries against Block’s data warehouse in response to natural language requests from non-technical staff. It uses an MCP connector to a Databricks server to execute these queries and return results.
• Internal Service Integration: Block created custom MCP servers for internal services like their “Beacon” feature store. This “teaches” Goose how to submit code to Beacon, allowing the AI to directly interact with their machine learning pipelines.
• Operations Automation: Operations teams use Goose to automate tasks like closing support tickets by retrieving necessary data and taking actions (e.g., updating a ticketing system) through dedicated MCP connectors to Jira.

Block’s adoption of MCP as a standardization layer has contributed to a more maintainable system architecture. Internal APIs, when exposed through MCP servers, become accessible to the Goose agent. The company reports that implementing this agent-based workflow has led to changes in their operational processes and data interaction methods in both engineering and non-engineering contexts..

Case Study 2: MCP in Developer Tools and IDEs

AI-powered developer tools are increasingly adopting MCP to provide richer, context-aware coding assistance. Companies like Windsurf (formerly Codeium) , Anysphere, Replit, and Sourcegraph are embedding MCP to allow AI coding assistants to seamlessly access project context, documentation, and development environments.

Consider an IDE plugin with an AI agent:

• The agent (MCP client) connects to various MCP servers: a Git server for repository access, a documentation server for API references, and possibly a Sourcegraph server for semantic code search.
• When a developer asks, “Where is the user authentication logic?” the agent can:

  1. Call the Git MCP tool to search the codebase.
  2. Use a Sourcegraph MCP tool for a more in-depth semantic understanding if needed.
  3. Fetch relevant internal design documents or API specs via a documentation MCP tool.

• The agent then synthesizes this information from multiple sources to provide a comprehensive answer, all orchestrated through standardized MCP calls. This allows the AI to “understand” the project context deeply and offer more relevant assistance.

Integrating MCP with Open-Source Agent Frameworks

Several open-source agent frameworks have begun to incorporate support for MCP. Here are a few code examples for MCP usage in LangChain, CrewAI and AutoGen.

LangChain

LangChain is a versatile framework for building LLM-powered applications. It simplifies the integration of LLMs with external tools, databases, and APIs, enabling developers to create sophisticated conversational agents, knowledge retrieval systems, and automation workflows. The framework has been used for building chatbots, facilitating retrieval-augmented generation, and synthetic data generation.

The langchain-mcp-adapters package (Python and TypeScript) allows LangChain agents to load tools from any MCP server as if they were native LangChain tools. For example, suppose you have an MCP server that provides math operations as tools. With the adapter, you can connect to that server and incorporate its tools into a LangChain agent in just a few steps:

Code snippet:

```
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
from langchain_mcp_adapters.tools import load_mcp_tools
from langchain.agents import initialize_agent
from langchain.chat_models import ChatOpenAI


# Define how to start the MCP server (here via local stdio process)
server_params = StdioServerParameters(
    command="python", 
    args=["/path/to/math_server.py"]  # MCP server providing math tools
)


# Connect to the MCP server and load its tools
async with stdio_client(server_params) as (read, write):
    async with ClientSession(read, write) as session:
        await session.initialize()
        tools = await load_mcp_tools(session)      # Convert MCP tools to LangChain tools


# Create a LangChain agent with these tools
llm = ChatOpenAI(model_name="gpt-4")              # e.g. use GPT-4 via OpenAI API
agent = initialize_agent(tools, llm, agent="zero-shot-react-description")


result = agent.run("What is (3 + 5) * 12?")        # Agent can use the math tools to compute this
print(result)  # Expected output: 96
```

CrewAI

CrewAI is a Python framework designed for orchestrating multi-agent “crews” consisting of collaborative AI agents. Each agent has a clearly defined role and tasks. This framework has been used for complex problem-solving tasks, such as automated content generation, software development workflows, and multi-agent planning scenarios.

The CrewAI Tools library now supports MCP, letting you treat MCP servers as tool providers for CrewAI agents. The integration is straightforward: you use the MCPServerAdapter to connect to an MCP server and automatically load its tools into your agent’s toolbelt.

Code snippet:

```
from crewai import Agent
from crewai_tools import MCPServerAdapter
from mcp import StdioServerParameters


# Define MCP server (local process via stdio transport)
server_params = StdioServerParameters(
    command="python3", 
    args=["servers/my_mcp_server.py"],
    env={"UV_PYTHON": "3.12", **os.environ},
)

# Connect to the MCP server and get its tools within a context manager
with MCPServerAdapter(server_params) as mcp_tools:
    print("Loaded MCP tools:", [tool.name for tool in mcp_tools])

    # Create a CrewAI agent that can use these tools
    my_agent = Agent(
        role="Researcher",
        goal="Find insights from our internal knowledge base and Slack.",
        backstory="Has access to enterprise KB and Slack via MCP.",
        tools=mcp_tools,      # provide the loaded MCP tools to the agent
        reasoning=True,
        verbose=True
    )
    # ... (proceed to add agent to a crew or run tasks)

```

AutoGen

AutoGen is an open-source framework (originating from Microsoft) for building multi-agent conversational workflows. It emphasizes simplicity, flexibility, and scalability, enabling rapid prototyping and deployment of automated workflows. Its design allows multiple LLM-based agents to chat and cooperate on tasks (e.g. a “Manager” agent and a “Worker” agent solving a problem together).

AutoGen provides an McpToolAdapter under the hood, exposed via a high-level mcp_server_tools() function. Code for using Anthropic’s “fetch” MCP server to fetch web content within an AutoGen might look like this:

Code snippet:

```
import asyncio
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import StdioServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent

async def main():
    # Define the MCP server to use (e.g., a local web content fetcher)
    fetch_server = StdioServerParams(command="uvx", args=["mcp-server-fetch"])
   
   # Load MCP tools (here, a "fetch" tool)
   tools = await mcp_server_tools(fetch_server)   
   
   # Create an agent that has these tools available
   model_client = OpenAIChatCompletionClient(model="gpt-4o")  
   agent = AssistantAgent(name="fetcher", model_client=model_client, tools=tools)
   
   # Give the agent a task that requires using the fetch tool
   result = await agent.run(task="Summarize the content of https://example.com/article")
   print(result) 
asyncio.run(main())
```

MCP’s Effect on Agent Capabilities

MCP brings about improvements in how AI agents can operate and goes beyond providing tools to AI agents. The additional capabilities unlocked by MCP are:

1. Enhanced Agent Memory and State Persistence:
   

   • Externalized Long-Term Memory: Agents can use MCP Resources (such as vector databases or file systems) to store and retrieve information beyond the LLM’s limited context window. This enables true long-term memory and learning. Unlike RAG, where the source corpus is pre-curated and often static, the agent can dynamically determine what to store, how to structure the knowledge and when to update it on the MCP server. The agent can store disparate information from multiple different sources and can refine the stored knowledge when new information is available. It can also store details like user preferences, conversation history and task states to make future interactions with the agent feel more natural.
   • Shared Context Across Tool Calls: Within an MCP session, an agent can maintain context across multiple tool invocations. For example, if an agent plans an event, information gathered from a calendar tool via MCP can be used when invoking an email tool, also via MCP, without re-prompting or losing the thread.
   • Persistent Task State: For multi-step tasks, agents can save their progress or intermediate results to an MCP resource, allowing them to resume work later or hand off tasks to other agents.

2. Tool Interoperability and True Modularity:

   • Mix-and-Match Capabilities: Agents can connect to multiple MCP servers simultaneously, dynamically composing a suite of tools from diverse sources (e.g., cloud APIs, internal databases, local file systems) within a single operational context. Similarly, different AI Agents with different underlying LLMs can connect to the same MCP server seamlessly.
   • Dynamic Tool Discovery: Agents can programmatically query an MCP server for its available tools, their descriptions, and input schemas. This enables more adaptive agent behavior, allowing an agent to decide at runtime which tools are best suited for a given task.

3. Foundation for Advanced Multi-Agent Workflows and Communication:

   • Shared Workspaces & Toolkits: MCP can provide a common ground for teams of specialized agents. One agent might research and save findings to an MCP document store, which another agent then accesses to generate a report. Theoretically, a sufficiently powerful enough agent should be able to do both. But having specialized agents gives developers the freedom to focus on specific functionalities. When report generation requires deep domain knowledge, the underlying LLM for the report generation agent can be prompt-engineered or fine-tuned to generate reports in a specific way. The research agent can be agnostic to the report generation and can have powerful search and retrieval capabilities.
   • Agent-as-a-Tool: An entire agent’s functionality can be wrapped and exposed as an MCP tool. A “Validator Agent” could offer a validate_output(text) tool via MCP, callable by any other agent needing verification services. This supports hierarchical and modular agent designs. The agent can take in a detailed prompt with specific instructions. It can also call different tools or data sources based on the input provided, without needing to be explicitly programmed. Its capabilities can evolve under the hood to support multi-modal data and ingest new policies for validation without changing the external interface.
   • Orchestrated Multi-Step Processes: Complex business workflows often involve multiple systems and can be executed through various agents specializing in different tasks. Tasks can be transferred from agent to agent, with MCP serving as the backbone.

Observed Applications and Adoption of MCP

The Model Context Protocol is beginning to be implemented in various practical settings, demonstrating its utility beyond conceptual frameworks:

• Enterprise Automation at Scale: The Block “Goose” agent is a real-world example where MCP facilitates connections between an internal AI assistant and multiple existing backend systems. The agent can perform tasks like data querying and automating processes, which can contribute to operational efficiencies.
• Contextual Enhancement for Developer Tools: The integration of MCP into IDEs and developer platforms is creating AI coding assistants that have more capabilities than code auto-complete. By accessing real-time project context (codebase structure, dependencies, documentation, build status) via MCP, these assistants can provide more powerful and accurate code generation and comprehensive answers to complex queries about the code.
• Orchestration of Multi-System Workflows: Businesses are using MCP to automate complex workflows that span multiple SaaS applications and internal systems. For example, a sales operations workflow might involve an agent parsing leads from emails (via an MCP email tool), creating records in a CRM (via an MCP Salesforce tool), notifying the team on Slack (via an MCP Slack tool), and scheduling follow-ups (via an MCP calendar tool) – all managed through a unified MCP interface.
• Fostering an Open Ecosystem: MCP is open-source, which encourages community contributions. There is also a growing number of open-source MCP servers for various common tools and services, which are seeing rapid adoption by the AI developer community.

While MCP is still in its early stages, it is generating significant interest and experiencing widespread adoption in the real world. It is well on its way to standardizing how AI agents interact with external data, tools, and services.

Conclusion

Model Context Protocol is an important development in the AI agent landscape. By providing a standardized, open interface for agents to connect with tools, data, and even other agents, MCP addresses one of the most significant challenges in building practical, powerful AI systems: the complexity of integration.

For practitioners, engineers, and researchers, MCP offers a pragmatic path towards:

• Reduced Development Overhead: Spend less time writing bespoke integration code and more time focusing on agent intelligence and workflow design.
• Increased Modularity and Reusability: Build tool integrations once and use them across multiple agents, frameworks, and LLMs.
• Enhanced Agent Capabilities: Equip agents with persistent memory, the ability to use diverse tools seamlessly, and the foundation for sophisticated multi-agent collaboration.
• Future-Proof Architectures: Design AI systems that are more adaptable to the rapidly changing landscape of LLMs and agent technologies.

MCP is not a replacement for agent reasoning or orchestration frameworks like LangChain, CrewAI, or AutoGen. Instead, it supercharges them by providing a unified, robust “connectivity layer”. As the protocol matures and the ecosystem of MCP servers expands, we can expect to see even more innovative applications, ranging from highly personalized AI assistants that manage our digital lives to complex, autonomous agent systems tackling large-scale enterprise and scientific challenges. The bridges for a more connected and capable AI future are being built with MCP, and now is an exciting time for the community to leverage and contribute to this evolving standard.