Client
API
API Contract
Client asks. API responds. Your UI updates.
MCP gives AI agents a consistent contract to discover and call external capabilities.

What Is MCP?

Model Context Protocol (MCP) is a standard way for AI models to connect to tools and data sources. Think: one protocol, many integrations.

MCP Client and MCP Server

An AI app (client) connects to one or more MCP servers. Each server exposes tools, resources, and prompts the model can use.

Discover Tools Dynamically

Instead of hardcoding every integration, the client can discover tool definitions at runtime and pass those capabilities to the model.

Call a Tool Safely

When the model decides to use a tool, the client executes a structured call and returns the result back into the conversation loop.

Security and Trust Boundaries

MCP servers can expose sensitive operations. Apply auth, per-tool permission checks, and audit logs before allowing execution.

Resources Feed Context

Resources are structured context sources (docs, files, schemas). The model can read them without inventing or guessing missing facts.

Handle Tool Failures Gracefully

Tool calls can fail due to timeouts, validation errors, or permissions. Return structured errors so the model can recover intelligently.

MCP in AI Product Design

MCP lets you scale agent features faster: swap providers, add tools quickly, and keep a clean architecture for AI-native workflows.

MCP Architecture Understood

You can now reason about MCP-based AI integrations: discovery, tool execution, resource context, and security boundaries.

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