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Mcp-use – Connect any LLM to any MCP

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Mcp-use – Connect any LLM to any MCP

Hey Pietro and Luigi here, we are the authors of mcp-use ( https://github.com/mcp-use/mcp-use ). When the first MCP servers came out we were very excited about the technology, but as soon as we wanted to get our hands dirty, we found out that MCP could be used only through Claude Desktop or Cursor. As engineers, we did not like that. MCP seemed like something you wanted to use to build products and applications yourself, not something to hide behind a closed source application. So we approached the SDK but were pretty dissatisfied with the developer experience (double async loops, lots of boilerplate). We decided to write mcp-use to make our lives easier. mcp-use lets you connect any LLM to any MCP server in just 6 lines of code. We provide a high level abstraction over the official MCP SDK that makes your life easier and supports all the functionalities of the protocol. Demo video here: https://www.youtube.com/watch?v=nL_B6LZAsp4 . The key abstractions we provide are called MCPClient and MCPAgent. MCPClient takes in a set of server configurations, automatically detects the transport type and creates a background task which handles the stream from/to the server. MCPAgent is a combination of the MCPClient, an LLM, and a custom system prompt. It consumes the MCP client by transforming the tools, resources and prompts into model agnostic tools that can be called by the LLM. The library also contains some cool utilities: - secure sandboxed execution of MCP servers (we know the protocol doesn't shine for security) - meta-tools that allow the agent to search over available servers and tools (to avoid context flooding) and connect dynamically to the server it needs (you could create the omnipotent agent with this). Some cool things we did with this: - write an agent that can use a browser and create/read linear tickets updated with latest information on the internet - write an agent that has access to the metrics of our company to automatically create weekly reports. - I connected an agent to an IKEA curtain I hacked an MCP on to adapt the lighting of my room from images of the lighting situation. - recreated am open source claude code like CLI, with full MCP capability but with custom models and BYOK ( https://github.com/mcp-use/mcp-use-cli ). We recently crossed 100,000 download and we are used by many organizations, including NASA! We’d love to hear what you think of it, most importantly how we can improve it! We are happy to answer any questions and look forward to your comments.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, cursor, claude · Missing: mac, agents, macos
97%97% 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: supports, created, including · Missing: reddit linkedin, podcasting, latex
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, open source, ide · Missing: https docs, just released, exist
48%48% 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
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · 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
13%13% 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.

Incorrect prediction on native model

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