We’ve built MCP servers for calculators and a cafe. Today we chose what to build for real: a project solid enough to present as a professional proof of concept.
1. The Shortlist
Every option followed the same pattern: an MCP server over operational data, and an agent that answers questions about it. No real data is available for any of them, so all of them run on synthetic data.
| Option | The kind of question an agent should answer |
|---|---|
| UPI payment gateway monitor | “Why did SBI’s success rate drop in the last five minutes?” |
| Quick commerce operations | “Which Hyderabad stores will run out of lemons before evening?” |
| Delivery fleet tracker | “Which orders will miss the 30-minute promise?” |
| SRE and CI failure triage | “Did the 3:30 deploy cause the checkout errors?” · “Is build 4.2.1’s failure new or known?” |
| Contact centre operations | “Which angry customers have waited longest?” |
| EV charging network | “Where are fast chargers on the way to the airport?” |
What makes a good MCP project: a question that today needs someone to open three dashboards and correlate them by hand.
2. The Decision: An Agentic Observability Proof of Concept
We’ll build an MCP server that retrieves live metrics from a running application, so an AI agent can carry out simple SRE tasks.
Why this, not UPI? A payment demo is easy to recognise as a toy. An observability layer over a real application has the depth an interviewer expects, and it maps onto work many of you already do.
Make it yours. The rental app is a stand-in. Replace it with an application from your own organisation, and the proof of concept becomes something you can describe from experience.
3. The Architecture
| Component | Role |
|---|---|
| The rental app | A Python FastAPI application, provided via GitHub |
| Database | PostgreSQL or SQLite |
| Load generator | Simulates user traffic, since there are no real customers |
| Chaos generator | Injects faults, such as network disconnections, to see whether the agent can trace them |
| MCP server | Exposes the metrics as tools an agent can call |
| Docker | Runs every component in containers, so setup is the same on every machine |
Deployment is flexible. The MCP server can sit on the same machine as the app, or on a separate one that serves several app instances.
Authentication comes last. The server starts anonymous; access control is added at the end, as the final layer.
4. MCP Doesn’t Replace Observability. It Drives It.
The goal isn’t to replace mature tools. Monitoring stacks stay. MCP lets an agent use them.
| Today | With an MCP layer |
|---|---|
| A person reads static graphs | Ask in plain language: “Why is the application slow?” |
| Someone correlates metrics by hand | The agent calls the right tools and correlates the results |
| A human handles every alert | The agent handles simple cases: it notifies a person, or fixes the issue |
The point is to take people out of the simple, repetitive SRE work, not out of the loop entirely.
5. Design Rules for the Server
Don’t expose every endpoint. Wrapping a whole API one-to-one as MCP tools is an anti-pattern. Expose a few high-value tools that answer the questions an operator actually asks. Every extra tool is something the model has to read and choose between.
Know where the tokens go. Running an MCP tool costs nothing in tokens. The LLM spends tokens deciding which tool to call, and again reading what comes back. Lean tools and compact results keep costs down.
6. Claude, Standard or Enterprise
The protocol is the same. Connecting a server is JSON configuration in settings, whichever plan you’re on. What differs in an enterprise is policy: your organisation’s IT rules decide which servers you may connect, not a different version of MCP.
7. Terms That Came Up
Agentic RAG
- Plain RAG retrieves once, then answers.
- Agentic RAG lets the agent control retrieval: deciding what to fetch, from where, whether the results are good enough, and whether to fetch again. It can also take actions along the way.
A2A (agent-to-agent)
- How agents work with other agents.
- We’ll cover it after at least two complete agent projects, so there’s something real to connect.
✅ Action Items
- Install Docker before the next session. A setup guide for Windows and Mac will be shared. On Windows, Docker Desktop needs WSL 2, so enable that first.
- Watch for the GitHub link. It will contain the rental app, the Docker files and a README.
- Pick an application from your own work that could replace the rental app in your version.
- Write three questions you’d want an agent to answer about that application, in plain English. Those become your first tools.
⏭️ Next up: standing up the rental app, the load generator and the first MCP tools.
