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Mcp Challenges Atomic Habits Platform Teams Vs Central Teams And Accelerate Capabilities

Jun 23, 2025

Monday Ideas — Edition #161
͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­
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Hey, Luca here, welcome to a new edition of Refactoring! Every week you get:

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How to Design APIs for an AI World 🔌

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MCP challenges, atomic habits, platform teams vs central teams, and Accelerate capabilities💡

Monday Ideas — Edition #161

Luca Rossi
Jun 23
 
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1) Does MCP address AI API challenges? 📈

This idea is brought to you by today’s sponsor — Postman!

Last week we published a long piece on how LLMs are changing how we design APIs.

MCP is obviously trying to address this, but how much does it really help? To be fair, it tackles some of the challenges:

  • 🔍 Discovery and capabilities — MCP servers explicitly declare what they can do, solving the "how does AI know what's available" problem.

  • ❌ Standardized errors — the protocol defines consistent error formats that AI can interpret.

  • ⬜ Stateless operations — each MCP call is self-contained, aligning with AI's non-deterministic nature.

But it doesn’t address others:

  • 💸 Token economy — MCP doesn't specify (or recommend) token-efficient formats; verbose (and thus expensive) responses remain verbose.

  • ⏱️ Latency optimization — no built-in batching or streaming optimizations for slow LLM workflows.

  • 🩹 Self-healing hints — while errors are standardized, MCP doesn't provide standard avenues for recovery suggestions or alternative endpoints.

To me, this is largely ok, because 1) it is unclear whether the scope of MCP should include all or any of these, and 2) at this stage it’s more important to converge on a standard than to design a perfect standard.

We explored more ideas about AI-first APIs in the full article 👇

Refactoring
How to Design APIs for an AI World 🔌
As engineers, we've been building applications for different audiences throughout the decades…
Read more
12 days ago · 63 likes · 4 comments · Luca Rossi

Also, Postman recently launched an MCP catalog, making it easier for developers to find and share MCP servers. They also debuted their own MCP generator, to easily create MCP servers.

Check out Postman MCP Generator


2) Atomic Habits Video! 📙

Our book reviews have always been among the most popular Refactoring articles (check out our latest one, on The Manager’s Path!), so I just tried to do something new by turning one into a Youtube video!

I picked Atomic Habits, because 1) it is one of my favorite books, and 2) I believe it is useful to just about everyone, regardless of their job. Here is the video 👇

You can also find the full newsletter article below 👇

Refactoring
Atomic Habits 📙
Hey there! This is a Book Edition 📗 — where I publish my review + summary of a famous non-fiction book in our space…
Read more
2 years ago · 40 likes · Luca Rossi

3) 🧱 Platform teams should never lose sight of their customers

Earlier this year Camille Fournier published a full article on Refactoring on how to create good platform engineering teams.

She wrote at length about how platform teams are fundamentally different from the classic central teams, and one of the core differences is that central teams often lose sight of their customers.

Instead of thinking about the people who use their systems as their customers, they view them as those clueless application engineers who just don’t get it. They don’t read the docs, they don’t know how to use systems in the right way, they don’t want to try the new stuff and give feedback on it.

Treating your customers as an inconvenience to be managed is one of the main contributors to the bad reputation of central teams.

We need to view our platforms as products not just because we want them to be thoughtful abstractions that are easy to use but also because we want to make sure that we are building things that the customer actually wants and needs.

Your team will have lots of good ideas for products you could be building, but in order for those products to be successful they need to be evaluated for product market fit: will the application engineers at your company actually use this thing once you build it?

You can make something that seems great on paper, with easy onboarding, great docs, and widespread customer awareness, but still get no adoption because it just doesn’t meet a pressing need for the application teams.

A simplified model of the Platform Engineering lifecycle

This is more than just hiring some product managers, making a product roadmap, setting some adoption metrics, and calling it a day. Your whole platform team needs to develop customer empathy and connections with customer teams who can give you feedback on what is important to them and where their pain points lie.

Your best products may even come from application teams who have built something useful for themselves that turns out to be something you could expand for the rest of the company.

You can find the full article by Camille below 👇

Refactoring
Creating a Platform Engineering Team 🧱
Hey, Luca here! This is the first article we are publishing from our brand new guest author program, where some of the best engineering writers in the world contribute to Refactoring with original pieces…
Read more
5 months ago · 42 likes · 1 comment · Luca Rossi

4) 📊 Accelerate is more than DORA

Ask anyone about Accelerate, and chances are they will mention the DORA metrics.

These four KPIs define how teams can measure their software delivery performance, and became instantly famous:

  1. 🚀 Deployment Frequency — how often you release to production.

  2. ⏱️ Lead Time for Changes — the amount of time it takes a commit to get to production.

  3. 📉 Change Failure Rate — the percentage of deployments causing a failure.

  4. 🛠️ Time to Restore Service (MTTR) — how long it takes to recover from a failure.

One of the reasons why the metrics caught on is because they provided, for the first time, a research-backed way to evaluate software delivery across two dimensions:

  • Throughput → via Deployment Frequency + Lead Time for Changes.

  • Stability → via Change Failure Rate + MTTR.

But here’s the thing: if you think Accelerate is only about metrics, you're missing 90% of the picture. The core of Accelerate is not the metrics: it's the engine that enables them.

The book meticulously identifies and validates 24 key capabilities that have been statistically shown to improve software delivery performance. The metrics are the outcome, while the capabilities are the drivers. And the research proves this connection with extreme rigor. It moves the conversation from "what good looks like" to "what specific actions demonstrably lead to good.

With some degree of simplification, we can organize these capabilities into three buckets: cultural, process, and technical.

Some of the 24 capabilities identified by Accelerate, in an ideal journey that goes from culture, to process, to tech.

These buckets work as levels of a pyramid, each one supporting the health of the ones above:

  1. Good culture is what makes people work well together and feel good about their work environment. It keeps retention high, stress low, and enables the creation of good process 👇

  2. Process exists to make work flow well through the system. Good process is about tight feedback loops and minimizing waste.

  3. Good culture and good process naturally lead to the technical practices that enable elite software delivery, like continuous deployment and empowered teams.

We reviewed Accelerate in our book club two months ago, and we reviewed all the 24 capabilities in our full book review 👇

Refactoring
Accelerate 📗
Every two months we read a book in our community book club, read it on our own, and review it together in a lively online event…
Read more
2 months ago · 36 likes · Luca Rossi

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I wish you a great week! ☀️
Luca

 
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