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Chainlit Now Supports Anthropic's Model Context Protocol (MCP)

Hacker News

Chainlit Now Supports Anthropic's Model Context Protocol (MCP)

Hi everyone, I’m the co-founder of Chainlit. Chainlit is an open-source python framework to build conversational LLM applications in minutes! There are lots of MCP servers but only few ways to leverage them. Super excited to announce that Chainlit now supports Anthropic’s Model Context Protocol! It means you can easily build a client application and customize UI/UX as well as python backend logic. And, it already integrates with popular AI agent frameworks. Cookbook example with Linear MCP: https://github.com/Chainlit/cookbook/tree/main/mcp-linear Cookbook example with Stripe MCP: https://github.com/Chainlit/cookbook/tree/main/mcp Docs: https://docs.chainlit.io/advanced-features/mcp Looking for some feedback from folks building with MCP!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, mcp · Missing: mac, agents, macos
91%91% 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.
Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist
60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
57%57% 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
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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