Outlook MCP – I accidentally made the best email assistant

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I couldn't understand the hype around MCP so I decided to build a simple server for Outlook (requires admin in Azure). For the build I just used a Python MCP SDK: https://github.com/modelcontextprotocol/python-sdk Now, I'm not a fast dev. Still, in 2 days I had Claude Desktop filing and responding to emails better than any assistant I've worked with (human or otherwise). None of this was due to my skill. I was basically just wrapping msgraph calls in the same @mcp.tool decorator over and over. In fact, I spent more time fiddling with msgraph auth & api-calls than I did on the MCP server itself. Today, in a typical workflow, I just tell Claude to "sort my inbox" and it handles everything from filing mails, creating folders, writing responses, scheduling meetings, flagging actions, etc. For anyone wondering about MCP, the following struck me: 1. It was the fastest "time-to-capable" I've had on any software build. 2. It was fully "human-capable" in a way that I have not experienced with "agentic workflow" approaches... it just worked. 3. There are nuances to how you "architect" your tool signatures for best results. For example, I had to name one parameter html_code_mail_body to prevent Claude from using plaintext in mails. 4. It was better to make pure, highly cohesive and atomic tools and let the LLM combine them for more complicated tasks. 5. It was so easy to build, all companies are likely to launch official MCP servers eventually. Overall, I think MCP offers a stark alternative to the "agentic workflows" model. In a technical sense, each step in a workflow is highly coupled to its upstream/downstream counterpart. This makes them both rigid and fragile. That's the source of some of the "RPA smells" people talk about with workflow agents. MCP is classic dependency inversion. The llm becomes an abstraction that can self-orchestrate using any tool it wants, in any order, based on the task set for it.

Visit product View on Hacker NewsJul 21, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: calls · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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