What is an MCP Server? Model Context Protocol Explained

If you're building AI agents or working with large language models (LLMs), you've probably run into a familiar problem: your AI assistant can understand natural language, write code, and analyze text, but getting it to actually do things? Connect to your database, search your files, or call your internal APIs? That's where it falls apart.

MCP servers fix this. They're the connection layer between AI systems and external systems like databases, APIs, and file storage.

The Model Context Protocol, Explained

The Model Context Protocol (MCP) is an open standard from Anthropic that gives LLMs a universal way to connect with external data sources and tools. The analogy that gets thrown around is "USB-C for AI," and it's apt: one standardized interface that works across different models and applications.

Before MCP, every AI integration was custom. Want Claude to access your database? Build a custom integration. Want it to search your files? Another custom integration. Want it to run SQL queries? Same story. Every combination of AI model and data source required its own connector.

MCP changes this with a common protocol. Build one MCP server for your data source, and any MCP-compatible AI client can use it.

How MCP Architecture Works

MCP uses a client-server architecture with three components:

MCP Hosts are the AI applications where users interact. Claude Desktop, an AI-powered IDE, or a custom chatbot you've built. The host contains the LLM and manages conversations with users.

MCP Clients live inside the host application. They handle communication with MCP servers, discover available tools, and translate between what the LLM needs and what the protocol requires. When the LLM decides it needs external data, the client makes it happen.

MCP Servers are the external services that provide capabilities to the AI. Each server exposes specific tools, data, or functionality through a standardized interface. A server might connect to a database, a file system, an API, or any other resource the AI needs.

How an MCP Server Works

In practice, it flows like this: a user asks the AI assistant a question in natural language. The LLM recognizes it needs external information. The MCP client queries available servers to find relevant tools. The server executes the request and returns results in real-time. The LLM incorporates that context into its response.

The underlying communication uses JSON-RPC, a lightweight protocol that keeps things fast.

What MCP Servers Actually Do

MCP servers expose three types of capabilities to AI models:

Tools

Tools are functions the LLM can call to perform actions. A tool might search a database, send an email, create a calendar event, or execute an SQL query. Each tool has a defined schema specifying its inputs and outputs, so the LLM knows exactly how to use it.

Tools can also enforce permissions, requiring user consent before executing sensitive actions. This keeps humans in the loop when the AI needs to do something consequential, like deleting files or sending messages. For applications handling sensitive data, these permission controls are essential for security and compliance.

A GitHub MCP server, for example, might expose tools like create_issue, search_repositories, or get_pull_request. The LLM can invoke these when a user asks something like "create a bug report for the login issue we discussed."

Resources

Resources provide data the LLM can read for context. Unlike tools, which perform actions, resources are primarily for information retrieval. A resource might be a file, a database record, a document, or any structured data the AI needs to understand.

Resources help the LLM answer questions like "what does our API documentation say about authentication?" without the user copy-pasting content into the chat.

Prompts

Prompts are reusable templates that servers can provide. They give users or the LLM pre-built ways to interact with the server's capabilities. A prompt might be a specific workflow for generating reports or a template for common queries.

Why MCP Matters for AI Development

The core problem MCP solves is the M×N integration nightmare.

Without a standard protocol, M different AI applications and N different data sources means M×N custom integrations. Every new AI tool requires connectors for every data source. Every new data source requires connectors for every AI tool. The complexity explodes.

MCP reduces this to M+N. Each AI application implements the MCP client protocol once. Each data source implements an MCP server once. Everything connects.

For developers building AI agents, this means you can focus on your agent's logic instead of writing integration code. Need file system access? Drop in an existing MCP server. Need database queries? There's a server for that.

For organizations with existing tools, you can expose internal systems to AI assistants without rebuilding everything. Wrap your existing APIs in an MCP server, and any AI tool in your organization can use them.

For the AI ecosystem, a standard protocol means more reusable components, better tooling, and faster innovation. The community can build and share MCP servers that benefit everyone.

Common Use Cases

Connecting AI Assistants to Internal Data

The most immediate use case is giving AI assistants access to your organization's knowledge. Instead of copying documents into chat windows, you deploy MCP servers that connect to your data sources:

A Google Drive server that lets the AI search and read your documents
A Slack server that provides context from team conversations
A database server that can query your internal systems
A file system server for local development work

The AI assistant can now answer questions using your actual data, not just its training knowledge.

Building AI-Powered Workflows

MCP enables AI agents to execute multi-step workflows across multiple systems. An AI agent could:

Search your CRM for recent customer complaints
Query your database for related product issues
Check your documentation for known solutions
Create a support ticket with recommended actions
Send a summary to the relevant team channel

Each step uses a different MCP server, but the agent orchestrates them together.

This pattern applies across industries. In healthcare, an AI assistant could pull patient records, check clinical datasets, and generate summaries for physicians, all through MCP connections to different systems while maintaining proper access controls.

RAG and Knowledge Retrieval

Retrieval-Augmented Generation (RAG) is one of the most effective patterns for AI applications. Instead of relying solely on training data, the LLM retrieves relevant information at query time.

MCP servers fit naturally into RAG architectures. A knowledge base MCP server can:

Accept semantic search queries from the LLM
Search vector embeddings to find relevant content
Return contextualized results the LLM can use in its response

This keeps your AI assistant's answers grounded in your actual documentation, reducing hallucinations and improving accuracy.

Agentic Coding and Development Tools

Development environments are adopting MCP quickly. IDEs like Cursor, GitHub Copilot, Windsurf, and tools like Claude Code use MCP servers to give AI coding assistants access to:

File system operations (read, write, search)
Git operations (commits, branches, diffs)
Terminal commands
Database queries
API testing

This transforms AI from a code-suggestion tool into an agent that can actually build, test, and deploy software.

Getting Started with MCP Servers

Using Existing Servers

The fastest path is using pre-built MCP servers. The open source ecosystem already has servers for:

File systems: Read and write local files
Databases: PostgreSQL, SQLite, and others
APIs: GitHub, Slack, Google Drive, Notion
Search: Web search, semantic search
Development: Git, terminal, Docker

The Model Context Protocol organization on GitHub maintains official SDKs and reference implementations, and the community servers repository has dozens of pre-built options. Check what exists before building custom.

Building Custom Servers

When you need to connect AI to your own systems, you'll build a custom MCP server. The official SDKs support Python and TypeScript:

BASH
1# Python
2pip install mcp

BASH
1# TypeScript  
2npm install @modelcontextprotocol/sdk

A basic server defines its tools, resources, and prompts, then handles incoming requests. The SDKs abstract away the protocol details so you can focus on your business logic.

Here's the skeleton of a TypeScript MCP server:

TYPESCRIPT
1import { Server } from "@modelcontextprotocol/sdk/server/index.js";
2
3const server = new Server({
4  name: "my-mcp-server",
5  version: "1.0.0"
6}, {
7  capabilities: {
8    tools: {},
9    resources: {}
10  }
11});
12
13// Define your tools and resources here
14
15server.connect(transport);

The MCP documentation at modelcontextprotocol.io covers implementation details, transport options (stdio, HTTP, SSE), and best practices.

For deployment, MCP servers can run anywhere: your own infrastructure, cloud platforms, or edge networks like Cloudflare Workers for low-latency global access.

Managed MCP Solutions

Building and hosting MCP servers takes work, especially for RAG use cases that require vector databases, embedding pipelines, and retrieval infrastructure. Managed platforms handle this complexity so you can focus on your AI application.

For knowledge-base and RAG scenarios, platforms like Orchata provide MCP-native endpoints out of the box. Upload your documents, and Orchata handles the embedding, indexing, and retrieval. Your AI agents connect via MCP and get semantic search capabilities without building the infrastructure yourself.

This makes sense when you want the benefits of MCP (standardized integration, reusable connections) without maintaining the servers and vector databases that power them.

What's Next for MCP

The Model Context Protocol is early but gaining momentum. OpenAI, Google, and other major providers are adopting or supporting MCP, signaling it's becoming a real standard rather than a single-vendor effort. Google's Gemini already supports connected apps, and the pattern is spreading across the industry.

Expect to see:

More pre-built servers as the ecosystem expands
Better tooling for debugging, testing, and deployment
Hosted MCP services for common use cases
Advanced patterns like multi-agent architectures where AI agents communicate through MCP

If you're building AI applications today, MCP is worth learning. It's the clearest path to connecting LLMs with real-world data and actions.

Frequently Asked Questions

What is an MCP server?

An MCP server is a service that exposes tools, data, and capabilities to AI applications through the Model Context Protocol. It acts as a bridge between large language models and external systems like databases, APIs, and file storage.

What does MCP stand for?

Model Context Protocol. It's an open standard developed by Anthropic for connecting AI models to external data sources and tools.

What does the MCP do?

It standardizes how AI applications communicate with external services. Instead of building custom integrations for every AI-tool combination, developers build to a common protocol that works across different LLMs and applications.

Is the MCP server an actual server?

Yes and no. An MCP server is a real service that runs and responds to requests, but it doesn't have to be a traditional always-on server. MCP servers can run as local processes (launched on demand), as HTTP services, or as serverless functions. The MCP specification supports multiple transport mechanisms including stdio, HTTP, and Server-Sent Events (SSE).

How do MCP servers work?

MCP servers receive requests from MCP clients (which live inside AI applications), execute the requested action (querying a database, searching files, etc.), and return results. Communication happens over JSON-RPC, a lightweight protocol that keeps interactions fast. See the official MCP documentation for implementation details.

What is the Model Context Protocol (MCP)?

An open standard that creates a universal interface between AI models and external tools. Anthropic developed and open-sourced it, and the specification is publicly available.

Can MCP servers use their own LLMs?

MCP servers don't typically run LLMs themselves. They provide tools and data to LLMs. That said, nothing stops an MCP server from calling an LLM as part of its processing. A server could use an LLM to summarize documents before returning results, for example.

What are AI agents?

LLM-powered applications that can take autonomous actions to accomplish goals. Unlike simple chatbots that just respond to prompts, agents can use tools, make decisions, and execute multi-step workflows. MCP servers give agents access to the tools they need to act in the real world.

Why does the MCP server matter in AI workflows?

MCP servers enable AI workflows that span multiple systems. An agent can query a database, update a CRM, send notifications, and generate reports by calling different MCP servers. Without MCP, each integration requires custom code. With MCP, you compose workflows from reusable, standardized components.

How does MCP relate to API security?

MCP includes built-in concepts for permissions and user consent. Servers can require approval before executing sensitive actions, and the protocol supports authentication between clients and servers. For applications handling sensitive data, MCP's permission model helps maintain security and compliance. The host application controls what servers are connected and what permissions they have.

What role does an MCP server play in network management?

MCP servers can expose network management capabilities to AI assistants: querying infrastructure status, managing configurations, monitoring services. The Render MCP server lets AI assistants manage cloud deployments, for example. The server handles the actual network operations while the AI provides the natural language interface.

What are the benefits of using an MCP server?

Standardization, reusability, and ecosystem growth. Build one server for your data source, and it works with any MCP-compatible AI application. Use pre-built servers from the community repository instead of building from scratch. As more tools adopt MCP, your integrations become more valuable.

What are the key features of an MCP server?

MCP servers expose three types of capabilities:

Tools: Functions the AI can call to perform actions (queries, API calls, file operations)
Resources: Data the AI can read for context (documents, database records, configurations)
Prompts: Reusable templates for common interactions

Servers also support real-time communication, permission controls, and multiple transport protocols. IDE integrations like Windsurf's Cascade show how these features work in practice.

Building AI agents that need access to knowledge bases or documents? Orchata provides MCP-native RAG infrastructure so you can skip the complex setup and start building.

What is an MCP Server?