Insight: How MCP Connects Any LLM to Any Cloud
Modern systems aren’t neat and tidy. They span AWS, Azure, GCP, and even on-prem boxes hidden under someone’s desk. They involve multiple teams, multiple APIs, and increasingly, multiple AI models. And that’s where most integrations fall apart: every LLM expects different input, every cloud has its quirks, and no one wants to hand their infrastructure over to a chatbot.
Enter MCP — the Model Control Plane. It’s the quiet middle layer that makes everything talk to everything else, safely and on your terms. You can think of it as the adapter between your cloud tools and the intelligence layer running on Claude, ChatGPT, Gemini, or whatever LLM you choose next year.
Why MCP Matters
MCP sits between the LLM and your real systems. Instead of letting the model hallucinate a database query or guess how your cloud infrastructure works, you give it structured tools — small, focused Python functions registered on the MCP server. When the LLM wants something done, it sends a clear request. MCP decides what gets run, with what arguments, and what gets returned.
This lets you:
One Bridge, Many Roads
An MCP server doesn’t care whether the tool behind the scenes talks to AWS, Azure, GCP, or your local SQLite database. It just needs to return a result. That makes MCP the perfect control layer for hybrid, multi-cloud, messy-real-world setups.
You can have Claude trigger a function that hits Azure Blob. Or ChatGPT can ask for EC2 instances from LocalStack. Or Gemini can call a function that queries Redshift. The LLM doesn’t need to know — and that’s the point. It doesn’t have to.
MCP gives you the connective tissue that glues LLMs to real infrastructure. And in a world where nothing is homogeneous, that makes it more than useful. It makes it essential.
Enter MCP — the Model Control Plane. It’s the quiet middle layer that makes everything talk to everything else, safely and on your terms. You can think of it as the adapter between your cloud tools and the intelligence layer running on Claude, ChatGPT, Gemini, or whatever LLM you choose next year.
Why MCP Matters
MCP sits between the LLM and your real systems. Instead of letting the model hallucinate a database query or guess how your cloud infrastructure works, you give it structured tools — small, focused Python functions registered on the MCP server. When the LLM wants something done, it sends a clear request. MCP decides what gets run, with what arguments, and what gets returned.
This lets you:
- Keep secrets and credentials away from the model
- Control what actions are allowed
- Route requests to the right cloud, the right SDK, the right endpoint
- Swap in mocks or test data when running locally
One Bridge, Many Roads
An MCP server doesn’t care whether the tool behind the scenes talks to AWS, Azure, GCP, or your local SQLite database. It just needs to return a result. That makes MCP the perfect control layer for hybrid, multi-cloud, messy-real-world setups.
You can have Claude trigger a function that hits Azure Blob. Or ChatGPT can ask for EC2 instances from LocalStack. Or Gemini can call a function that queries Redshift. The LLM doesn’t need to know — and that’s the point. It doesn’t have to.
MCP gives you the connective tissue that glues LLMs to real infrastructure. And in a world where nothing is homogeneous, that makes it more than useful. It makes it essential.
Case Examples: Real-World and Imagined
Claude + AWS (Standard)
A DevOps engineer builds a tool in MCP that lets Claude list EC2 instances or purge old S3 files. Claude becomes a smart assistant in the team's Slack channel, responding to queries like, "Which EC2 instances are running in us-east-1?"
Gemini + GCP (Standard)
A data team uses Gemini to interact with BigQuery through MCP. The model can generate queries, trigger jobs, and summarize results, without ever seeing a credential or accessing the raw infrastructure.
ChatGPT + Azure (Standard)
An IT support team connects ChatGPT to MCP tools that interface with Azure AD, letting the model help with user provisioning, VM status checks, or blob storage usage summaries.
Claude + Azure (Oddball)
A mixed cloud org prefers Claude's reasoning for finance workflows but stores sensitive documents in Azure Blob. MCP bridges the gap, giving Claude structured, read-only access to the needed files.
Gemini + AWS (Oddball)
A startup wants to use Gemini’s multimodal capabilities to trigger AWS Lambda workflows via MCP. Gemini reads a product image and asks MCP to upload metadata to an S3 bucket and trigger a classification job.
ChatGPT + GCP (Oddball)
A customer service chatbot running on ChatGPT needs access to regional marketing data stored in GCS. MCP handles the request and transforms the response before returning it to ChatGPT.
Hybrid: Claude + AWS + On-Prem
A manufacturing company uses Claude to orchestrate parts ordering. MCP connects to an AWS-hosted supply chain API and also calls a local Python script on an on-prem database to get inventory counts.
Hybrid: ChatGPT + Azure + GCP
A multi-cloud company uses ChatGPT to generate daily reports. MCP fetches usage data from Azure Monitor and financial logs from GCP's BigQuery, merges them, and formats the result as a polished PDF.
These aren’t far-fetched. They’re coming. And with MCP, you don’t need to wait until the future is perfectly integrated. You just need to give your models the right tools to talk to the world you've already got.
Claude + AWS (Standard)
A DevOps engineer builds a tool in MCP that lets Claude list EC2 instances or purge old S3 files. Claude becomes a smart assistant in the team's Slack channel, responding to queries like, "Which EC2 instances are running in us-east-1?"
Gemini + GCP (Standard)
A data team uses Gemini to interact with BigQuery through MCP. The model can generate queries, trigger jobs, and summarize results, without ever seeing a credential or accessing the raw infrastructure.
ChatGPT + Azure (Standard)
An IT support team connects ChatGPT to MCP tools that interface with Azure AD, letting the model help with user provisioning, VM status checks, or blob storage usage summaries.
Claude + Azure (Oddball)
A mixed cloud org prefers Claude's reasoning for finance workflows but stores sensitive documents in Azure Blob. MCP bridges the gap, giving Claude structured, read-only access to the needed files.
Gemini + AWS (Oddball)
A startup wants to use Gemini’s multimodal capabilities to trigger AWS Lambda workflows via MCP. Gemini reads a product image and asks MCP to upload metadata to an S3 bucket and trigger a classification job.
ChatGPT + GCP (Oddball)
A customer service chatbot running on ChatGPT needs access to regional marketing data stored in GCS. MCP handles the request and transforms the response before returning it to ChatGPT.
Hybrid: Claude + AWS + On-Prem
A manufacturing company uses Claude to orchestrate parts ordering. MCP connects to an AWS-hosted supply chain API and also calls a local Python script on an on-prem database to get inventory counts.
Hybrid: ChatGPT + Azure + GCP
A multi-cloud company uses ChatGPT to generate daily reports. MCP fetches usage data from Azure Monitor and financial logs from GCP's BigQuery, merges them, and formats the result as a polished PDF.
These aren’t far-fetched. They’re coming. And with MCP, you don’t need to wait until the future is perfectly integrated. You just need to give your models the right tools to talk to the world you've already got.
Conclusion
The future of AI integration isn’t about committing to one model or one cloud. It’s about building smart, flexible bridges — and MCP is that bridge. Whether you’re cleaning up local data, orchestrating cloud services, or stitching together on-prem and SaaS tools, MCP keeps the complexity under control and lets your LLMs shine where they’re strongest.
In a landscape full of silos, connectors matter. MCP is your connector. Lightweight, modular, and cloud-agnostic, it doesn’t care which LLM you use or where your data lives. It just works.
That’s why it’s the unsung hero — and why it won’t stay unsung for long.
The future of AI integration isn’t about committing to one model or one cloud. It’s about building smart, flexible bridges — and MCP is that bridge. Whether you’re cleaning up local data, orchestrating cloud services, or stitching together on-prem and SaaS tools, MCP keeps the complexity under control and lets your LLMs shine where they’re strongest.
In a landscape full of silos, connectors matter. MCP is your connector. Lightweight, modular, and cloud-agnostic, it doesn’t care which LLM you use or where your data lives. It just works.
That’s why it’s the unsung hero — and why it won’t stay unsung for long.
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Written by Aaron Rose, software engineer and technology writer at Tech-Reader.blog.
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