Model Context Protocol (MCP), Explained

Why MCP Exists

• Without MCP, each AI app and each tool connector usually needs custom integration code.
• MCP defines a shared interface so clients can talk to many tools and servers the same way.
• This reduces integration cost, improves portability, and speeds up ecosystem growth.

MCP Architecture (Conceptual)

1. Host / Client: the AI app or assistant runtime.
2. MCP Server: exposes tools, resources, and prompts in MCP format.
3. External Systems: SaaS APIs, internal services, files, databases.

Think of MCP as USB-C for AI tools: one protocol, many compatible integrations.

Core Capabilities

• Tools: callable operations such as create ticket or run query.
• Resources: structured context and data the model can consume.
• Prompts: reusable prompt templates exposed by the server.

Security Model (Practical)

• Explicit permission boundaries between model runtime and external systems.
• Server-side policy controls for auth, scope, logging, and rate limits.
• Safer than ad-hoc direct tool wiring because contracts are explicit and auditable.

Adoption Path

1. Start with one narrow use case, such as issue tracking or docs retrieval.
2. Wrap the backend capability in an MCP server with a clear tool contract.
3. Add authentication and audit logs before broader rollout.
4. Iterate tool definitions based on real production prompts.

References

• Stytch: Model Context Protocol Introduction
• Official MCP docs: Introduction