Tsar-MCP – How to Create an MCPServer Aspect in C/C++

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Greetings, This is a link to our learning guide to make your own MCP server in C/C++ (with no other dependencies). If you wanted to make one from scratch but needed some insight into dealing with MCP messages: the I/O flow, JSON parsing, and request dispatch, grab the teaching version tagged mcp/teaching/v1.0.0 in the repository. The JSON parsing in C would have been a hurdle, but then there's this amusing story: Eric: "Let's create a helloWorld MCP server in C." Gemini: "That will be somewhat difficult, there are easier ways." Eric: "Why is it difficult??" (the second '?' was a bit of my ego). Gemini: "Well, you'll need to parse JSON, handle raw I/O, and return strict JSON-RPC back to the client." Eric: "One moment....", attaching a decade-old JSON parser engine file JSONParser.h Amusingly, the AI read the code and completely shifted its tone... Gemini: "Oh, well then it's not so difficult." The head revision plus the aspect guide gets you a self-diagnosing, asynchronous, enterprise-ready baseline to be able to concentrate on what you really want your own MCP Server to do. I would begin with the repository README.md though, because it's actually as easy to use as it describes. Cheers, ... Eric

Visit product View on Hacker NewsJun 28, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mcp, gemini, using · Missing: mac, agents, macos

74%74% 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: gemini · Missing: supports, reddit linkedin, podcasting

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews

34%34% predicted probability of success on AppSumo, 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

33%33% 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

28%28% 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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