MCP Architecture Explained for Developers

The MCP architecture at a glance

MCP has three main layers: the client (AI agent), the MCP server (tool gateway), and the backing systems (APIs, databases, files). Each layer has a clear responsibility, which keeps tool calling safe and predictable.

Control plane vs data plane

The control plane is where tools are described, permissions are declared, and policies are enforced. The data plane is where actual tool calls are executed and responses are returned.

Keeping these planes separate means you can update tool metadata and permissions without touching the underlying API implementation.

Core components

• Tool catalog: metadata describing what tools exist.
• Schemas: structured inputs and outputs for each tool.
• Auth boundary: token and scope enforcement per tool.
• Execution layer: the system that runs the tool call.
• Observability: logs and metrics for auditing calls.

Schema lifecycle

Tool schemas are derived from API contracts, typically OpenAPI. Once generated, the schemas should be versioned and validated on every update to avoid breaking existing tool calls.

LegacyAI manages this lifecycle by regenerating schemas from your spec and flagging any breaking changes before you publish.

How Claude calls a tool

Claude Desktop reads the tool catalog, selects a tool, and sends a structured request to the MCP server. The MCP server validates inputs, applies auth, and forwards the call to your API. Results return in a structured format that the agent can interpret.

1. Agent discovers tools from the MCP server.
2. Agent prepares a tool call with validated parameters.
3. MCP server authenticates and executes against your API.
4. Response is normalized and returned to the agent.

Architecture diagram (text description)

Picture a horizontal flow: on the left, the AI agent UI. In the middle, the MCP server with three stacked blocks labeled Tool Catalog, Auth + Validation, and Execution. On the right, your APIs and data stores. A monitoring branch leaves the MCP server and goes to logs and metrics.

Observability pipeline

MCP servers should emit structured logs that include tool name, parameters, response time, and status. This makes it possible to trace tool behavior and debug unexpected responses.

For enterprise deployments, route logs to a SIEM or monitoring system so security teams can audit tool usage over time.

Why the separation matters

By keeping tool discovery separate from execution, MCP allows you to control what an agent can do without rewriting your APIs. It also makes auditing and governance explicit, which is critical for enterprise deployments.

Security boundary

The MCP server is the policy enforcement point. It should validate inputs, apply auth checks, and block unsafe requests before they reach your systems.

This boundary lets you change tool permissions without changing backend services, which is essential for safe rollout.

How LegacyAI implements MCP

LegacyAI generates the tool catalog, schemas, and auth mappings directly from your OpenAPI spec. It also gives you controls for read-only defaults, scoped access, and dynamic sync so the architecture stays aligned with your API.