I think the original SDK is quite low level, not assembly low level, but if you want to get started and expose a tool to an LLM you would have to write something in the neighborhood of ~200 lines of code, and they would have to be wrapped in a double nested async loop. If you want to develop authorization with Dynamic Client Registration, it going to be another ~1000 lines. I think many developers will want to avoid that. Plus, if the spec changes, and it does very often, you would have to keep track of it, we take care of that for you.

As somebody who has done both- mcp-use makes it very very simple.

If you are at all familiar w/ python back-end development, it's literally just as easy use this tool to make an API as it is to put your MCP into claude desktop.

MCP SDK is not hard to do per se... but it's more than a 5 minute job

We did! Basically we have a meta layer between the Agent and the MCP servers you give to the clients.

We call this server manager, basically instead of exposing all the tools from all the servers at once to the agent we only expose 4 meta tools: - list servers() - connect to server(server_name) - search_tool(query) - disconnect from server(server_name)

So that the agent can dynamically connect to specific servers without flooding its context with all the tools.

The search tool basically performs semantic search over all the tools from all the servers returning the top N results (tools) and the server they belong to so that the agent can connect to the right server.

A demo of this is here https://www.reddit.com/r/mcp/comments/1k598v9/give_your_agen... where I hid a useful tool in a sea of useless ones (10 useful, 3000 useless) and the agent was able to find and use the right one.

I answered this question in a similar case a few months ago: https://news.ycombinator.com/item?id=43111524. If you read that and still have a question that isn't answered there, let me know!

Valid feedback, we did not put as much work on the agent as we did for the client and MCP side yet, but we plan to write a cleaner more composable MCPAgent as well. Actually I would be super happy to hear what you consider a standard here, something like https://openai.github.io/openai-agents-python/ ?

Why is dependence on LangChain an issue?

Not that I disagree necessarily, just wondering if there's a consensus that LangChain is too opinionated/bloated/whatever for real industry applications, or if there's some other reason.

Hey, thanks for your detailed feedback, it's helpful to be challenged. Here are some quick thoughts:

One: We've heard similar feedback on Langchain dependency. We also heard it is still widely used in enterprise settings despite its limitations. We're actively exploring alternatives, but it is a pretty crowded space and we could not find the best alternative yet. I'm curious: is there a specific solution you'd recommend? Shall we have our own LLM layer ?

Two: Agreed on observability client-side, improvements are on our roadmap, though currently prioritized lower based on user needs. Regarding security, we manage server-side risks via sandboxes, tool restrictions, and upcoming access control features. Preventing tool poisoning client-side is something we'll look into further. You have any other specific suggestions client side ?

Three: The "infinite server" scenario is not the main focus on the library, I am sorry if that is how is sounded. It is more of an interesting solution to an interesting problem that we wanted to share.

Four: Totally agree on prioritizing quality over quantity. mcp-use emphasizes flexibility and ease of integration, not necessarily connecting to numerous servers. Reliability is a server-client joint effort, when we work with companies this is one of our main focuses.

Five: While similar solutions exist, we've found our approach resonates well with users based on adoption and feedback. That said, this is not the end of the road, we are working and talking with many companies and solving many of the issues you mentioned, most are not so easy to integrate in the library and we offer only as part of our cloud offering.

Thanks again for challenging our thinking! And please if you have inputs it'd be great to have them

That is nice, is this like a local E2B ?

Thanks, I appreciate that!

For sanboxing we are using E2B, which spins up a sandbox for the server execution, we run the mcp server command inside the sandbox and expose the stdio stream as an sse from the sandbox remote. You could potentially replace the E2B sanbox with any other. Unfortunately, mcp servers that need access to your filesystem cannot be run on a sanbdox, I believe you could do it with the network, what would be the use case?

Hey, thanks! Please let us know if we can help with anything once you get started. FastMCP is very good at servers, they just recently started to provide a client.

The client is comparable, but we support multiple connections at once, we handle task management for you (so you do not need to async with in our code), and we have an agent that you can get started with. Also I do not think FastMCP will go into the agent side of things in the Agent - Client - Server spectrum, so if you need support on that side (integrations with other frameworks, providers etc) you'll find it here :)

Hey jijji, fair point, I assumed that the hn audience and/or the people interacting with the website would already know about MCP.

If you're not familiar, MCP is an open-source protocol created by Anthropic and later adopted by all major model providers. It defines how LLMs communicate with external applications and services.

More specifically, MCP standardizes the interaction between an MCP server, where the business logic resides, and a client, loosely speaking an LLM that consumes it. The MCP server exposes primitives like tools, resources, and prompts, which the client can use to perform various operations. The communication protocol used is JSON-RPC 2.0, and interaction between the client and server occurs either locally through stdio streams or remotely over streamable HTTP.

Here are some of the good things about it:

Easier integration into agents: From an agent development perspective, MCP simplifies integrating external capabilities. Previously, you'd have to write custom integrations, which is sometimes difficult or limited by the availability of public APIs. Now, you can directly plug in an MCP server, if provided. if you had to write a custom integration, using MCP is beneficial as well, as it separates integration logic from agent logic, making it easy to hot swap any of the two.

Standardized interface and incentives for companies: Because MCP standardizes communication, developers can create MCP servers independently from the specific LLM or agent consuming them. This compatibility with clients like ChatGPT, Claude Desktop, and mcp-use provides a strong incentive for companies to develop MCP servers for their own applications. This is great because they are the ones that know best how to do this (and can).

Widespread Adoption: A protocol's value heavily depends on its adoption, and MCP is sticking real well. Companies, developers, and major model providers are mostly on board with MCP.

I'd love to know if somebody has other positive points or negative points about mcp and can share it here. Some are across the thread already.

what are you referring to ?

Yeah I don't even see the point of this. There's enough MCP clients now and the whole point of MCP is to make tools available to any LLM that can use tools.

It's most likely just over-exuberance from people who know the founders or use/love the project and want to support it. It doesn't look orchestrated.

(tomhow and I were both replying at the same time)

I see what you mean and yeah it often happens that friends or users of a project try to "help out" in this way. It's fine on many other platforms, so it's more of a misunderstanding than anything. People don't realize that it runs against HN's culture.

I've moved those comments to a stub post now (https://news.ycombinator.com/item?id=44752791).

We designed this slightly differently where one can use the client directly an not only in our agent, the structure is more focused around the MCP functionality.

Related, in the sense that it is also a way to connect any LLM to any MCP. Not sure what you meant, I.e when there’s a discussion of a feature I think it’s normal to mention other systems with similar features?

I see the confusion :) I meant my comment not as an actual reply to my own comment but more as an afterthought.

We detached this subthread from https://news.ycombinator.com/item?id=44751518 and marked it off topic.

We also detached https://news.ycombinator.com/item?id=44751518 from https://news.ycombinator.com/item?id=44751285 to make it a root comment, given that the author agreed it didn't make sense to be a reply (https://news.ycombinator.com/item?id=44751620).

Thank you for sharing this! At Product Weaver we love how mcp-use makes our life easier when accessing MCP servers like Jira, Linear, Notion and many others. We like the clean code we can write, as it's vital for maintaining it. Ad maiora!

Really great work! We’ve been integrating LLMs into production chat-based workflows for a while, and mcp-use has given us the biggest DX boost we’ve seen all year. It strips away the boilerplate of the official SDK, so spinning up a new MCP backend becomes almost a copy‑paste job.

From a company perspective that’s huge: adding a new chatbot feature that used to take a couple of sprints and a lot of glue code can now be done in hours instead of weeks.

Love this! Finally someone breaking open the MCP ecosystem for real dev use. mcp-use looks like the right balance between power and simplicity. The agent abstraction + tool discovery is super smart.

Also, hacking an IKEA curtain with MCP is an all-time flex.

Curious if you’re planning to build infra on top or keep it strictly dev tools?

Love this! This is super helpful!

Super cool project — love the direction you're taking with simplifying MCP integration and tooling! The search layer between agent and servers is a clever abstraction to reduce cognitive + compute load. Also appreciate the IKEA curtain flex

Would love to see how this evolves toward more dynamic infra setups. Keep it up!

belli e bravi

This is super cool! Building with MCP is such a hassle right now, this is a game changer.

nice. i feel interaction with mcps now is sort of broken, and building on top of the sdk is a pain. being compliant with the specification is a way better take.

i'm curious to see how this scales. and it's a good starting point on reshaping our interaction with basically every single application.

[flagged]

Good, I have not dig into the code, just one question: how to deal with other stuff supplied by MCP server, I mean besides tools, server also supplied "resources", "prompt" and other functionality, I saw some struggle here(https://github.com/vincent-pli/mcp-cli-host/blob/main/docs/r...) and here(https://github.com/vincent-pli/mcp-cli-host/blob/main/docs/p...),

Interesting project! Today, AI projects move progress forward. Good luck to you!

Nice! shoot us a message on Linkedin :)

Thanks for the mention! I'm the author of Mcp-error-formatter, which was mentioned above. Exactly right about the client/server complexity. I've been working on this exact problem. Client-side heuristics can catch timeouts and HTTP codes, but nuanced business logic errors need server cooperation.

The interesting part is dual error representation: devs need full stack traces for debugging, but LLMs need clean structured metadata for retry decisions. Plus you want error telemetry flowing to observability systems.

Question for the mcp-use authors: How do you handle error propagation in multi-server setups? If Server A times out but Server B is healthy, does the agent know which server failed and whether to retry or switch servers? And are you capturing error patterns to identify flaky servers or consistently failing tool calls? The retry orchestration across multiple MCP servers seems like a really interesting problem.

Hey thanks for the comment, the work from https://github.com/bjoaquinc/mcp-error-formatter is great. I will definitely put some version of this into one of the next releases. Just opened an issue mentioning this thread.

The problem with the package above is that it is server side, and I think it should be. Server is the one that knows (has responsibility to know) if a tool is retryable and has informations about the error, and fairly so.

I see though how some sort of improved error formatting could be introduced in the client as well, but it shouldn't contain any logic about the error, rather format the error in the best way possible (in the direction in which it is best understandable by LLMs).

We have it ! https://github.com/mcp-use/mcp-use-ts