Initially working with AI tools, I faced a common issue: each required unique integration. Connecting a language model to a database or accessing the filesystem meant writing custom code, making each a standalone project that didn’t transfer.

Model Context Protocol (MCP) standardizes AI access to external tools and data sources, acting as a universal adapter. Implementing MCP once allows AI to connect to any MCP-compatible server for database, file, API, or other services.

By the end of this article, you’ll understand why MCP exists, how it works conceptually, and what problems it solves for both AI application developers and the users who rely on AI tools.

Why MCP Exists

AI applications need context to be useful. A language model limited to its training data is insufficient. Real-world applications must read files, query databases, call APIs, and interact with external systems, but building these integrations is costly and repetitive.

The problem impacts developers and users: developers waste time redoing integrations, and users face inconsistent experiences, missing features, and slower innovation because developers focus on integration work instead of user-facing improvements.

The Developer Problem

Before MCP, every AI application developer faced the same challenges:

  • Custom integration code for every tool. Each database, file system, or API needed its own connection logic.
  • No standardization. One application’s database integration looked nothing like another’s.
  • Maintenance burden. When tools changed, each custom integration had to be updated.
  • Limited reusability. Code written for one application couldn’t be shared with others.

MCP offers a standard protocol for AI applications to connect to any MCP-compatible server. You implement it once, then connect to any server speaking MCP, avoiding custom integration code.

The User Problem

Users face different but related problems:

  • Inconsistent experiences. Each AI application handles tasks differently due to varying integration approaches.
  • Missing capabilities. Developers can’t include all integrations, leaving users with gaps.
  • Slower innovation. When developers focus on integration code, they have less time for desired features.
  • Vendor lock-in. Without standards, switching between AI apps means losing key integrations.

MCP offers a standard protocol for AI applications to connect to any MCP-compatible server. You implement it once, then connect to any server speaking MCP, avoiding custom integration code while adding more capabilities for users.

Think of MCP like USB for AI applications: developers create a single integration, and users can access it across any compatible AI application, just as USB allows devices to work with any computer.

How MCP Works

MCP uses a client-server architecture in which AI applications connect to MCP servers that provide tools, data, and services. It is built on JSON-RPC 2.0, a standard for request and response formats.

The Architecture

MCP has three main components that work together:

Host: The application embeds the language model, which could be an IDE, chatbot, or any app requiring AI. The host connects to MCP servers and manages the interaction flow.

The application embeds the language model, like an IDE, chatbot, or AI app. The host connects to MCP servers and controls interactions.

Client: Formats requests and communicates with MCP servers, handling protocol details such as message formatting, error handling, and transport. It translates the host’s needs into MCP messages.

Server: Provides access to external resources, including database queries, file operations, API calls, or other capabilities. Servers implement the MCP protocol and handle client requests.

The relationship is simple: hosts need capabilities, clients request them, and servers provide them. This separation lets any host connect to any server without custom code.

Here’s how they work together: When you ask an AI app to search for a file, the host (your app) needs that ability. The client sends your request as an MCP message to a file server. The server searches and returns results. The client formats the response, and the host displays it. This all happens via the MCP protocol, so the host doesn’t need to know how the server searches files.

The Protocol Layer

MCP uses JSON-RPC 2.0 as its communication protocol because it’s language-agnostic, well-understood, and supports both synchronous and asynchronous communication.

When a client uses a server’s capability, it sends a JSON-RPC request with a method name (like tools/call or resources/read) and parameters. The server processes the request and returns a response with a result or error.

The protocol functions as a remote procedure call: you invoke a method by name, pass parameters, and receive a result. The method resides on a remote server and communicates via JSON-RPC rather than direct calls. This abstraction enables MCP to work across languages, networks, and deployments.

The protocol defines standard methods for three types of capabilities:

  • Tools: Executable functions servers expose, like searching files, querying databases, or calling APIs. They accept parameters and return results.

  • Resources: Read-only access to external data resources, which have URIs and can be files, database records, or other structured data. Resources are discoverable and subscribable for updates.

  • Prompts: Template messages guide user interactions, structuring conversations and ensuring consistent formatting. They include parameters that clients fill in dynamically.

This three-capability model covers most integration needs, with tools for actions, resources for data access, and prompts for interaction patterns.

Transport Mechanisms

MCP supports various transport mechanisms due to differing deployment needs.

STDIO (Standard Input/Output): Used for local processes and command-line tools, the client and server communicate via standard input/output. It’s simple and secure for local use due to no network exposure.

HTTP: Used for web and remote servers, clients send HTTP POST requests with JSON-RPC messages, requiring authentication and HTTPS encryption, enabling remote access and web integration.

Server-Sent Events (SSE): Used for real-time updates and streaming, like HTTP but with a persistent connection for event streams, enabling servers to push updates to clients.

All three transports use the same JSON-RPC 2.0 protocol; transport is just the delivery mechanism, allowing transport switching without changing application logic.

Key Relationships

MCP connects to several related concepts in AI application development:

MCP vs. Direct Integration

Direct integration involves custom code to connect AI applications to external systems. MCP replaces this with a standard protocol that requires MCP-compatible servers but allows connecting to any server without custom code.

Direct integration offers full control but needs maintenance; MCP sacrifices some control for standardization and reusability.

MCP vs. Plugin Systems

Many applications use plugin systems to extend functionality, but MCP is protocol-based rather than code-based. Plugins usually need code installation and version management. MCP servers operate independently and communicate over the network.

MCP is more flexible and easier to deploy in distributed systems but increases network latency and complexity versus in-process plugins.

MCP and JSON-RPC

MCP uses JSON-RPC 2.0, a mature, language-agnostic protocol, offering benefits like language independence, standard error handling, and request/response patterns. It can also utilize existing JSON-RPC tools and libraries.

The relationship is foundational: MCP is the application protocol that defines available capabilities and usage. JSON-RPC is the transport protocol that specifies message formatting and delivery. You don’t need deep JSON-RPC knowledge to use MCP, but knowing MCP builds on a standard protocol explains its cross-language/system compatibility.

Trade-offs and Limitations

MCP addresses real problems but isn’t suitable for all situations.

Benefits

For developers:

  • Standardization. Once you implement MCP, you can connect to any MCP server without custom code.
  • Reusability. MCP servers can be shared across applications and teams.
  • Separation of concerns. Applications focus on AI logic, servers focus on integration logic.
  • Language independence. Any language that supports JSON-RPC can participate.
  • Faster development. Connect to existing servers rather than build integrations from scratch.

For users:

  • Consistent experiences. The same tools work the same way across different AI applications.
  • More capabilities. As the MCP ecosystem grows, users get access to more tools and integrations.
  • Faster feature delivery. Developers add capabilities faster, so users get new features sooner.
  • Interoperability. Use the same tools and integrations across different AI applications.
  • Reduced lock-in. Standard protocols mean you’re not tied to a single vendor’s integration approach.

Costs and Limitations

  • Protocol overhead. JSON-RPC adds message formatting and parsing overhead compared to direct function calls.
  • Network latency. Remote servers introduce network round-trips that local code doesn’t have.
  • Server availability. You need MCP-compatible servers; without one, you require custom integration.
  • Learning curve. Teams need to understand MCP concepts and JSON-RPC patterns.
  • Debugging complexity. Distributed systems are harder to debug than local code.

When Not to Use MCP

MCP isn’t always the right choice:

  • Performance-critical paths. If you need microsecond-level performance, direct integration is faster.
  • Simple, one-off integrations. If you connect to one system and won’t reuse the code, custom integration may be simpler.
  • Tightly coupled systems. If your AI application and external system are always deployed together, direct integration might be better.
  • Legacy systems without MCP support. You still need custom integration for unsupported systems.

The goal isn’t to use MCP everywhere, but where standardization and reusability outweigh performance or simplicity.

Common Misconceptions

Several misconceptions about MCP cause confusion:

MCP is only for large applications

MCP suits projects of any size, including small ones, offering standardized, reusable servers. Its protocol overhead is minimal compared to the costs of custom integrations.

MCP requires complex infrastructure

MCP runs locally via STDIO, requiring no network, load balancers, or distributed systems. Many deployments are simple local processes.

MCP is a replacement for APIs

MCP doesn’t replace APIs; it standardizes access to them. MCP servers often wrap existing APIs to expose them through the protocol, but APIs are still necessary.

MCP servers are hard to build

MCP servers are easy to implement if you know JSON-RPC, which is well-documented with libraries in many languages. Building a basic MCP server is often easier than creating a custom integration.

MCP is only for AI applications

While MCP was designed for AI, its protocol is general-purpose. Any application needing standardized access to tools, resources, or prompts can use MCP. Its JSON-RPC foundation makes it language and domain-agnostic.

Conclusion

MCP addresses real problems for developers and users. It removes the need for developers to build custom integrations repeatedly and costs less. For users, it offers consistent experiences, faster innovation, and an expanding ecosystem.

MCP uses a JSON-RPC protocol allowing any AI app to connect to MCP-compatible servers without custom code. The architecture includes hosts with capabilities, clients that format requests, and servers that provide capabilities. It supports tools, resources, and prompts via various transport mechanisms, offering deployment flexibility.

Benefits include standardization, reusability, and ecosystem growth for developers and users. However, MCP isn’t always ideal. Performance-critical paths, simple integrations, and legacy systems may require custom solutions.

Understanding when standardization matters over performance or simplicity is key. MCP offers a better alternative to custom code for multiple AI integrations, enabling users to develop more capable applications faster.

Next Steps

Now that you understand MCP and its purpose, here’s what to do next.

  • Read the specification. The MCP Specification provides complete protocol details for implementation.
  • Explore the registry. The 🔎MCP Registry lists available servers you can use or learn from.
  • Study server implementations. Look at existing MCP servers to understand how they’re built in practice.
  • Check the GitHub organization. The MCP GitHub Organization provides examples, libraries, and community resources for building with MCP.

To implement, start with a simple server exposing one tool or resource. Protocol patterns become clear in code.

References