AI Integration Standard

What Is Model Context Protocol (MCP)?

MCP is the open standard that lets AI applications talk to external tools, databases, and APIs through a single universal protocol. Instead of building a custom integration for every system, you build once and connect everywhere.

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Quick Answer

Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Announced by Anthropic on November 25, 2024, MCP replaces fragmented, one-off AI integrations with a single standard — the way USB-C replaced a drawer full of proprietary cables.

SDKs

2 official (Python, TypeScript) + 4 community

Major Clients

Claude, ChatGPT, VS Code, Cursor, Claude Code

Primitives

Tools, Resources, Prompts

Open

License

Fully open-source specification

Why MCP Matters

Before MCP, every AI integration was a bespoke project. Want Claude to read your Postgres database? Build a custom connector. Want ChatGPT to search your company wiki? Build another one. Want Cursor to manage your GitHub issues? Yet another. Each integration was its own M×N problem — M AI applications times N data sources, each requiring unique code.

MCP collapses this into an M+N problem. Every AI application that speaks MCP can connect to every MCP server, just like every USB-C device works with every USB-C port. Build one MCP server for your system, and every compatible AI client can use it immediately.

The protocol was inspired by the Language Server Protocol (LSP), which solved the same fragmentation problem for code editors. Before LSP, every editor needed its own language plugin for every programming language. LSP standardized the interface, and MCP does the same for AI-to-tool communication.

The Core Insight

Just as USB-C provides a standardized way to connect electronic devices, MCP provides a standardized way to connect AI applications to external systems. One protocol, universal compatibility, no more bespoke integrations.

How MCP Works

MCP uses a client-server architecture built on JSON-RPC 2.0. The protocol defines three roles that work together:

Host

The host is the AI application the user interacts with — Claude Desktop, an IDE with AI features, or a custom AI agent. The host initiates connections and manages security boundaries. It decides which MCP servers to connect to and what permissions to grant.

Client

Each host contains one or more MCP clients. A client maintains a one-to-one connection with a single MCP server, handling protocol negotiation, message routing, and capability management. The client-server relationship is stateful — they maintain context across the session.

Server

MCP servers are lightweight programs that expose specific capabilities through the protocol. A server might provide access to a file system, a database, a web API, or any other external system. Servers declare what they offer (tools, resources, prompts) during an initial handshake.

Two Transport Mechanisms

Stdio

For local integrations. The host launches the MCP server as a subprocess and communicates over standard input/output. Fast, zero network overhead, ideal for desktop AI tools connecting to local file systems or databases.

Streamable HTTP

For remote integrations. The client connects to the server over HTTP with optional Server-Sent Events for streaming. Enables cloud-hosted MCP servers that multiple clients can reach over the network.

Technical Detail

All MCP communication uses JSON-RPC 2.0 as the wire format. This means structured request/response messages with typed parameters, error codes, and notification support — a well-understood standard that keeps implementations clean and interoperable.

The Three Primitives

MCP servers expose capabilities through three core primitives. Each serves a distinct purpose in how an AI application interacts with external systems:

Tools

Model-controlled

Tools are functions the AI model can invoke to take actions or retrieve computed results. Examples include running a database query, creating a GitHub issue, sending an email, or calling a web API. The AI decides when and how to use tools based on the user's request. Tools are the most common primitive and the backbone of agentic AI workflows.

Query a databaseSearch the webCreate a fileCall a REST API

Resources

Application-controlled

Resources are data sources the AI application can read — similar to GET endpoints in a REST API. They provide context (files, database records, API responses, screenshots) that gets attached to the conversation. Unlike tools, resources are typically selected by the application or user, not autonomously by the model.

File contentsDatabase recordsAPI response dataSystem logs

Prompts

User-controlled

Prompts are pre-built templates that define structured interactions for specific tasks. They allow MCP servers to offer reusable workflows — for example, a code review prompt that gathers the right context and asks the right questions. Users select prompts to guide the AI through a particular task with the right framing.

Code review templateData analysis workflowContent generation guideDebug checklist

MCP vs. Traditional API Integrations

MCP doesn't replace APIs — it standardizes how AI applications discover and use them. Here's how the two approaches compare:

Factor	Traditional API	MCP
Integration model	Custom code per service (M×N)	Universal protocol (M+N)
Discovery	Manual documentation reading	Automatic capability negotiation
Wire format	Varies (REST, GraphQL, gRPC)	JSON-RPC 2.0 always
Who invokes?	Developer writes integration logic	AI model selects and calls tools
Context handling	Developer manages state	Protocol maintains session context
New data source	Build new integration from scratch	Deploy MCP server, instant compatibility
Ecosystem lock-in	Tied to specific AI platform	Works with any MCP-compatible client
Standardization	No universal standard	Open spec (current: 2026-07-28)

Who Uses MCP

Since its November 2024 launch, MCP has been adopted across the AI ecosystem. These are the major platforms that support MCP as clients, enabling their users to connect to any MCP server:

Claude (Anthropic)

Anthropic’s AI assistant supports MCP natively in Claude Desktop and the Claude API, allowing direct connections to local and remote MCP servers for tool use and data access.

Claude Code

Anthropic’s CLI coding agent uses MCP extensively, connecting to file systems, version control, databases, and custom development tools through MCP servers.

ChatGPT (OpenAI)

OpenAI announced MCP support for ChatGPT in March 2025, enabling ChatGPT to connect to external tools and data sources through the same protocol.

VS Code (GitHub Copilot)

Microsoft’s Visual Studio Code supports MCP through GitHub Copilot’s agent mode, letting developers connect their IDE to custom MCP servers for enhanced AI-assisted coding.

Cursor

The AI-first code editor added MCP support early, allowing developers to extend Cursor’s AI capabilities with custom tools and data sources through MCP servers.

Open-Source Ecosystem

Hundreds of community-built MCP servers are available through the official registry at modelcontextprotocol.io, covering databases, cloud platforms, productivity tools, and more.

MCP and Your Business

MCP isn't just a developer tool — it has practical implications for any business that uses AI or plans to. Here are the concrete ways MCP changes the game:

Faster AI Integration

Instead of spending weeks building custom integrations for each AI tool, you build one MCP server for your system. Every MCP-compatible AI client — today and in the future — can connect to it immediately.

Better Security Controls

MCP includes built-in permission and consent mechanisms. Hosts control which servers can connect, what capabilities are exposed, and users approve sensitive operations. Security is part of the protocol, not an afterthought.

No Vendor Lock-In

Because MCP is an open standard, your integrations aren’t tied to any single AI provider. If you switch from Claude to ChatGPT (or use both), your MCP servers keep working. Your investment in integration is portable.

AI Agents That Actually Work

MCP is the infrastructure that makes agentic AI practical. When an AI agent needs to check a database, update a CRM, file a ticket, and send a notification — MCP provides the standardized tool access that makes multi-step workflows reliable.

Internal Knowledge Access

Connect your AI tools to internal documentation, wikis, databases, and proprietary data through MCP servers. Your AI assistant goes from knowing general information to knowing your business specifically.

Custom Tool Development

The official SDKs (TypeScript, Python, Java, Kotlin, C#, Swift) make it straightforward to build MCP servers tailored to your exact needs. If your team has a system, they can expose it through MCP.

Getting Started

The fastest path to MCP in your business: start with the official MCP server registry for off-the-shelf integrations (databases, Google Drive, Slack, GitHub), then build custom servers for your proprietary systems using the SDKs.

Frequently Asked Questions About MCP

Everything you need to know about Model Context Protocol, answered.

MCP stands for Model Context Protocol. It is an open standard that provides a universal way for AI applications (like Claude or ChatGPT) to connect to external data sources and tools through a single, standardized protocol instead of building one-off integrations for each system.

MCP was created by David Soria Parra and Justin Spahr-Summers at Anthropic. It was publicly announced on November 25, 2024. The protocol is open source and maintained as a community-driven specification under the Model Context Protocol organization on GitHub.

A traditional API is a custom interface specific to one service — every new integration requires its own implementation. MCP is a universal protocol: once an AI application supports MCP, it can connect to any MCP server without new integration code. Think of the difference between having a unique cable for every device versus USB-C working with everything.

MCP servers are lightweight programs that expose specific capabilities — data sources, tools, or prompts — through the standardized MCP protocol. For example, an MCP server might give an AI assistant access to a database, a file system, a CRM, or a web search API. Each server is focused on one system or domain and communicates with AI clients using JSON-RPC 2.0.

As of 2026, MCP is supported by Claude (Anthropic’s assistant), Claude Code, ChatGPT (OpenAI), Visual Studio Code (via Copilot), Cursor, Windsurf, and a growing number of AI-powered development tools. The open nature of the spec means any AI application can add MCP support.

Yes. MCP is fully open source. The specification, SDKs (available in TypeScript, Python, Java, Kotlin, C#, and Swift), and reference server implementations are all publicly available on GitHub under the modelcontextprotocol organization. Anyone can build MCP servers or clients.

If your business uses AI tools internally — for coding, customer support, data analysis, or workflow automation — MCP can dramatically simplify how those tools connect to your internal systems. Instead of building and maintaining custom integrations for each AI tool, you build one MCP server and every MCP-compatible AI client can use it.

The fastest way to start is to use an existing MCP server from the official registry at modelcontextprotocol.io. If you need a custom integration, the TypeScript and Python SDKs let you build an MCP server in under an hour. Anthropic’s documentation includes quickstart guides for both building and consuming MCP servers.

Ready to Connect AI to Your Business?

MCP is changing how businesses integrate AI. Whether you need custom MCP servers, AI agent development, or a strategy for AI-powered workflows — we can help you build it.