---
title: "Discussion on \"When MCP earns its overhead, and when a direct API is better\" | Hashnode"
description: "Discussion on \"When MCP earns its overhead, and when a direct API is better\". One question settles most of these arguments: will more than one AI application need this capability?"
url: "https://hashnode.com/posts/mcp-versus-direct-apis/6a6864171d202e6435ff9b70"
---

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# [When MCP earns its overhead, and when a direct API is better](https://blog.vamsiannamreddy.com/mcp-versus-direct-apis?utm_source=hashnode&utm_medium=feed)

One question settles most of these arguments: will more than one AI application need this capability?

Jul 28 · 3 min read · blog.vamsiannamreddy.com  
Updated 1d ago  
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-   [#mcp](https://hashnode.com/tag/mcp)
-   [#apis](https://hashnode.com/tag/apis)
-   [#function-calling](https://hashnode.com/tag/function-calling)
-   [#observability](https://hashnode.com/tag/observability)
-   [#production-ai](https://hashnode.com/tag/production-ai)

## Written by

[Vamsi Krishna](https://hashnode.com/@vamsikrishna981)

@vamsikrishna981 · Hyderabad, India

  
[View profile](https://hashnode.com/@vamsikrishna981)

Building production-ready AI systems and sharing practical field notes on LLMs, RAG, agents, evaluation, and governance.

I’m Vamsi, a builder focused on production AI systems. I write Production AI Field Notes to break down the architecture behind reliable LLM apps, RAG, agents, multi-agent workflows, evaluation, observability, safety, and governance. My goal is simple: help builders move beyond impressive AI demos and design systems that can be tested, operated, trusted, and improved in the real world.

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