AI infrastructure / explained
What is MCP?
The Model Context Protocol gives AI applications a standard way to connect with tools, files, databases, APIs, and real business systems.
Start with the problem
AI can think. But it needs a way to connect.
A language model can understand a request and produce an answer, but by itself it does not automatically know what is inside your computer, project files, company database, analytics account, or task manager.
Developers traditionally created a different custom integration for every model and every external service. That approach works, but it becomes difficult to maintain as the number of tools grows.
Instead of building a completely new connection every time, developers can expose a system through an MCP server. Compatible AI applications can then discover and use the capabilities that server provides.
The simple analogy
Think of MCP as a universal port for AI.
One connection pattern. Many systems.
USB-C gives different devices a shared connection standard. MCP plays a similar role for AI software: one structured way to connect models with many tools and data sources.
System architecture
Four parts work together.
MCP separates the AI application from the external service while giving both sides a predictable way to communicate.
Gives an instruction
Asks the AI to find information, update something, or complete a task.
Runs the AI experience
The application containing the model, interface, permissions, and conversation.
Maintains the connection
The host creates an MCP client to communicate with a specific MCP server.
Exposes capabilities
The server provides approved tools, resources, and prompts for an external system.
How a request moves
What happens when the AI uses MCP?
The host connects
The AI application connects to an approved MCP server and learns which capabilities that server supports.
Capabilities are discovered
The client can discover available tools, resources, and prompts instead of relying on a hard-coded list.
The model selects an action
When a tool is relevant, the model prepares structured arguments. The host can still apply permissions or ask the user for approval.
The server performs the work
The MCP server validates the request, communicates with the underlying service, and returns a result.
The AI explains the result
The result returns to the host, becomes part of the model’s context, and is translated into a useful response for the user.
The core building blocks
Tools, resources, and prompts.
These primitives serve different purposes and give the host control over how information and actions enter the conversation.
Tools
Executable functions the model can call, such as searching a database, creating a task, reading analytics, or updating a record.
Model controlledResources
Structured context the application can provide, such as file contents, documentation, database records, or repository history.
Application controlledPrompts
Reusable instruction templates that users intentionally select, such as a review workflow, report generator, or guided analysis.
User controlledA practical example
Claude Code connects to a project system.
Imagine connecting Claude Code to an MCP server that provides project information. Claude can discover the server’s tools, request the approved data, and help you act on it without requiring that entire integration to be built directly into Claude Code.
# Connect an HTTP MCP server
claude mcp add --transport http project-system https://example.com/mcp
# Ask Claude to use the connected system
“Show my active projects and identify the tasks that are overdue.”
✓ Connected to project-system
✓ Retrieved active projects
✓ Found 3 overdue tasks
Permissions still matter
A connection is useful only when it is controlled.
MCP does not automatically make every server safe.
A server may expose sensitive data or actions. Its permissions, authentication, implementation, and source still need to be reviewed.
- Connect only servers and services you trust.
- Give every server the minimum access it actually needs.
- Require confirmation before destructive or financial actions.
- Store API keys and secrets outside prompts and source code.
- Review tool descriptions, inputs, outputs, and audit logs.
- Separate read-only access from tools that can change data.
Where MCP becomes useful
One protocol. Many real workflows.
Connected coding assistants
Give an assistant controlled access to repositories, documentation, issue trackers, deployment systems, and development tools.
Business automation
Connect CRM records, internal dashboards, customer support systems, tasks, reports, and approval workflows.
Live information retrieval
Let an AI application retrieve approved information from databases, knowledge bases, files, and specialized search services.
Publishing systems
Read content records, prepare drafts, inspect assets, and publish approved changes through structured tools.
A useful distinction
What MCP is not.
MCP connects models to capabilities. It does not replace the model.
A server can expose database information, but MCP does not store that data itself.
The host and server still decide which actions and information are allowed.
The main idea
MCP makes AI useful beyond the chat box.
It gives AI applications a consistent language for discovering context, using tools, and working with real systems while leaving permissions and control in the surrounding application.
Explore the MCP documentation →