Building Reliable AI Agents with MCP

Model Context Protocol (MCP) is shifting how we think about AI tool integration. Instead of ad‑hoc function calling, we can define standard interfaces for agents.

The problem with ad‑hoc tools

We often start by manually defining tool definitions in JSON. It works for small scripts, but as the system grows, maintenance becomes a nightmare. Type safety is lost, versioning is nonexistent, and debugging feels like guessing.

We needed a better way. Enter MCP.

Standardizing the interface

By adopting a standard protocol, we decouple the agent’s reasoning engine from the tools it executes. This lets us swap implementations without retraining or reprompting.

{"name": "query_database", "description": "Executes a safe read-only SQL query", "input_schema": {"type": "object", "properties": {"sql": {"type": "string"}}}}

Key takeaways

• Decouple definitions from implementation.
• Enforce strict typing at the protocol level.
• Use standard error codes for agent feedback.