mc

mcp-golang – Build custom tools for LLMs in go

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mcp-golang – Build custom tools for LLMs in go

Anthropic released the Model Context Protocol a couple weeks ago - https://www.anthropic.com/news/model-context-protocol . They have official sdks for python and typescript but nothing for go, so I wrote one! The main goal is to provide a low boilerplate sdk to write MCP servers, you can spin up a server which can give an LLM access to weather forecasts like this: https://github.com/metoro-io/mcp-golang/blob/main/examples/g... . Using it here: https://youtu.be/xaMkMl_R-0A The library handles generating all the MCP endpoints and input schemas for the model by introspecting the types of the structs + annotations for the handlers. I'm currently working on filling out the rest of the optional server features in the protocol and hope to move on to a client implementation soon. If any folks find this interesting, help is always appreciated!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, mcp, new · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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