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Articles tagged with #mcp
Most MCP servers load everything eagerly, dump huge schemas into the system prompt, and make the agent pay for tools it will never call. The fix is boring: lazy, narrow, and spec-driven.
The default move when an agent gets something wrong is to paste more files into the context. But more files means more noise, and noise is what made it wrong in the first place.
You already have a working REST API. Hand-writing and maintaining a second, parallel set of AI tool wrappers is pure transcription that drifts. Generate MCP tools from your OpenAPI contract, start read-only, and keep authorization where it belongs - in your API.
Your AI coding assistant keeps using pageSize instead of limit and data.items instead of data.records because it guesses from pasted docs. Give it a local OpenAPI spec it can query over MCP, then verify multi-step scenarios against real requests and mock the missing dependency.
MCP connects one agent to tools and data. A2A connects autonomous agents to each other through discoverable, long-lived tasks. They are not competitors; they sit at different layers, and getting the boundary wrong is where agent architectures become unmaintainable.
Pointing an agent at forty internal MCP servers breaks tool limits and buries it in duplicated names. The pattern that works at scale is one authenticated gateway that aggregates specs, namespaces the tools, and keeps auth, logging, and rate limits in one place.
