MCP vs API: what changes when you ask AI about your own business data
An API needs someone to write code for each question. MCP lets your AI assistant use the API itself while you ask. What differs, and when you need which.
The HeyMetra Team Updated 4 min read
Key takeaways
- An API is how one program talks to one service. Someone writes code against it in advance, for questions they knew about when they wrote it.
- MCP, the Model Context Protocol, sits on top of APIs. An MCP server describes what it can do in words, and your AI assistant picks what to call while you ask, so a new question needs no new code.
- Most MCP servers call the service's ordinary API underneath, so MCP sits on top of APIs and lets an AI assistant use them.
- An API is still the right tool for fixed jobs that run on their own, such as a nightly sync or a dashboard that refreshes every hour.
- HeyMetra is a hosted MCP server over the APIs of more than twenty accounts, so you connect each account once and ask across them from Claude, ChatGPT or any other AI assistant.
An API lets one program read or change data in one service, and someone has to write the code for each job in advance. MCP, the Model Context Protocol, lets your AI assistant use those same APIs while you ask, so a new question needs no new code. This decides whether you need a developer to ask Claude or ChatGPT about your sales, your ads or your search traffic. This guide explains both in plain words and when each one fits.
What an API is
An API, or application programming interface, is a list of addresses a service opens to other programs. Stripe’s API has one for charges and one for refunds; Google Search Console’s has one for search queries. A program sends a request to an address with a key or a sign-in and gets data back.
APIs are precise and fast, and almost every business tool has one. The catch is who writes the request. A developer reads the documentation, picks the addresses, writes code and runs it. If you want a different answer next week, someone changes the code.
What MCP adds on top
MCP is an open standard that Anthropic published in November 2024. An MCP server takes one or more APIs and describes them as tools: a name, what the tool does, and what it needs. Your AI assistant reads those descriptions when it connects, and when you ask a question it picks the tools that answer it. The MCP specification calls this being “model-controlled”: the model discovers the tools and decides when to use them.
The API is still underneath. With MCP, your question decides which calls are made, at the moment you ask.
Here is a question that needs Search Console and Google Analytics together. Nobody wrote code for it:
Claude
Read Search Console and Google Analytics with HeyMetra | Page | Search clicks | Sign-ups |
|---|---|---|
| /blog/invoice-template | 5,100 | 6 |
| /blog/late-payment-email | 2,380 | 9 |
| /glossary/net-30 | 1,760 | 6 |
Built against the APIs, the same answer would mean calling the Search Console API for queries and pages, the Google Analytics Data API for sessions and key events, and writing code that matches the two by landing page.
MCP vs API side by side
| API | MCP | |
|---|---|---|
| Best for | Fixed jobs: exports, syncs, dashboards | Questions you did not plan for |
| A new question | Someone writes new code | No new code |
| Same result on every run | Yes, the code does the same thing each time | The data is read fresh and each answer is worded anew |
| Runs on a schedule, unattended | Yes | It answers when you ask |
| Questions across several accounts | Code that joins them | The AI assistant reads each account the question needs |
| What it costs | Developer time, then little to run | A subscription to a hosted server, or a server you run |
For what happens under the hood, our guide to MCP connectors compares a connector with an API integration step by step, and What is MCP? covers where the protocol came from.
When you still want an API
Code against an API is the better tool for jobs that never change:
- A nightly export of yesterday’s orders into your accounting system.
- A dashboard that refreshes every hour with the same twelve figures.
- A sync that copies new leads from your form into your CRM within seconds.
For those, code against the API is cheaper, faster and gives the same result every time. An AI assistant answering through MCP reads the data fresh for each question and words each answer anew. Use it for questions like “why did sign-ups drop on Tuesday?”, and keep a figure that must match to the cent in every monthly report in code.
Where HeyMetra fits
HeyMetra is a hosted MCP server over the APIs of the accounts you run your business on: Google Search Console, Google Analytics 4, Google Ads, Stripe, Slack, Zoho CRM, WooCommerce, AppsFlyer and more than twenty in all. You connect each one in HeyMetra with its own sign-in or key, which takes a few minutes, and add one address, https://mcp.heymetra.com/mcp, to Claude, ChatGPT, Grok, Perplexity, Claude Code, Codex, Cursor or Antigravity.
From then on a question can read every account it needs, and no one writes code for it. When a question asks for a change, such as pausing a campaign, your AI assistant writes out the exact change and nothing is sent until you approve it. Every plan starts with a seven-day free trial.
- #mcp
- #api
- #model-context-protocol
- #ai-assistants
Frequently asked questions
What is the difference between MCP and an API?
An API is a set of addresses a program calls to read or change data in one service, and a developer writes the calls in advance. MCP is an open standard that lets an AI assistant discover what a server can do and call it while you ask, so the question decides what is called.
Does MCP replace APIs?
No. An MCP server usually calls the service's API underneath. MCP adds a description of each tool that the AI model reads, plus a standard way for any AI assistant to connect to the server.
Is MCP just an API wrapper?
Partly. Many MCP servers wrap one API. A good one also decides what the AI assistant may do, holds the credentials so they never reach the chat, and returns answers the model can read without guessing, such as which currency a figure is in.
Do I need a developer to use MCP?
To build an MCP server, yes. To use one, no: you add its address to your AI assistant, sign in to the account, and ask in plain words.
When should I use an API instead of MCP?
When the job is the same every time and runs without anyone asking: a nightly export, a sync between two systems, a dashboard. Code against the API is cheaper and more predictable for that.