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These "researchers" don't need to practice music, don't need to practice art, don't need to practice law, don't need to practice coding, they can just be an LLM researchers/producers/owners and they get all the values that the other professions created.- All the above caused economic stagnation. People don't feel the need to pay other human beings, because everything is just a prompt away.- All the above caused stagnation of progress, or even winding
I recently did some tests on two MCPs with identical functionalities. Turns out one of them has really bad performance. So I wanna share those bad MCP design patterns that cause this.<p>It all started when I wrote an MCP Server (MCP-A) for a to-do list app. Later, the app officially released its own MCP Server (MCP-B). Both MCPs have the same functionalities and hit the same backend API.<p>The experiment is set up as follows:<p>- Both MCP Servers connect to the same ToDo list account, and it will be reset after each test. - 40 test prompts to simulate typical use cases for these MCPs. - The test was conducted with the same model, system prompt, and Agent framework<p>Here are the results:<p>| Metric | MCP-A | MCP-B | Gap | | ------------------- | ----------- | ----------- | ----- | | Tool Desc Length | 11,464 | 3,682 | — | | Pass Rate | 36/40 (90%) | 36/40 (90%) | Same | | Total input tokens | 637,244 | 3,174,329 | 4.98× | | Total output tokens | 17,301 | 23,238 | 1.34× | | Total Agent steps | 122 | 157 | 1.29× | | Total time | 597s | 676s | 1.13× |<p>---<p>The result shows that MCP-B took 35 more ReAct loops to complete 40 test cases compared to MCP-A, which means 30% more output tokens. I examined the log and found that the root cause is poor query tool design.<p>Take the `search tool` for example, its job is to find a todo item in the ToDo list. In MCP-B, this tool returns this:<p>{ "id": "6a1916b48f08cb3a4c857ed0", "title": "buy some groceries", "url": "https://todo.example.com/tasks/6a1916b48f08cb3a4c857ed0" }<p>But other CRUD operations require `project_id`, and `search_tool` doesn't return it. So the Agent has to call another tool `get_task_by_id`. On the other hand, MCP-A's query_tasks returns all necessary info to perform the next action in a single call:<p>Task 1: ID: 6a19143e8f084a8c8101612f Title: buy some groceries Project ID: 6a1914378f084a8c810160a9 Start Date: 2025-07-19 10:00:00 Priority: Medium Status: Active Unfiltered API Data was dumped into context window<p>If MCP returns pure API results to the Agent's context unprocessed, the Agent's context window will accumulate very fast.<p>Take MCP-B's `create_task` tool, for example. Its job is to create a to-do item. This is what this tool returns:<p>{ "id": "6a180de78f086bdead0608be", "projectId": "inbox125587327", ..... "createdTime": "2026-05-28T09:41:59+0000", "modifiedTime": "2026-05-28T09:41:59+0000", "focusSummaries": null }<p>These 600+ characters mean nothing to the Agent's task, but are still dumped into the Agent's context. On the other hand, MCP-A's create_tasks does a layer of filtering and formatting. This little tweak makes a huge difference in input token usage.<p>Another issue is tool count. More tools mean a larger candidate set for the model to choose from, which directly increases decision difficulty. In MCP-A, 47 tools were compressed down to 14, covering the same functionality with fewer tools.<p>---<p>So here are my takeaways on good MCP tool design: - When designing a tool, think about what the Agent will need next, not just what it's asking for right now. Return enough context in the result so the Agent can take the next action without making another round-trip.<p>- Too many tools will increase the model's decision burden. So it'd be better to minimize the number of tools within an MCP. Make sure they don't overlap functionalities.<p>- When your MCP returns data to the LLM, try to keep it LLM-friendly, which means readable. You can filter out unnecessary fields from the API response and format the data, rather than passing through raw JSON.<p>---<p>All the tests above were run by MCP-Eval. It's an MCP Server benchmarking tool. If you want to check your MCP's performance, feel free to check this out.<p>https://github.com/Code-MonkeyZhang/mcp-eval
As a high school student, I’m trying to figure out what major I’m interested in. About half a year ago, I thought EECS was a great major for some STEM students like me, because I see many of the world's most influential entrepreneurs, such as Elon Musk and Jensen Huang, have built companies around software, hardware, and artificial intelligence, helping advance technological and society.<p>Recently, however, I have been exposed to a variety of AI-powered programming tools, such as Claude Code and Codex. I was amazed by how capable these tools have become. They can generate websites, make software, even help solve hardware problems, and they can also help people with little programming experience build applications. On social media, I have already seen some people who have no coding background, but they use AI tools to create products and make money.<p>This experience has led me to question whether learning programming is still worthwhile. If AI can perform many coding tasks, why should people continue studying coding and computer science? Will coding still be an important skill in the future? like the next ten or twenty years, when I grow up.<p>At the same time, I wonder which fields of study will remain valuable as technology continues to evolve. Are there majors that are less likely to be affected by AI?Which fields will shape the next generation of innovation?<p>Personally, I do not yet have a definitive answer. However, I believe this is an important question for many STEM students today. because the rapid development of AI is changing how we think about education, careers, and the skills needed for the future.
I asked 4 working engineers this exact question on my podcast: a Google Developer Advocate (Stockholm), a Senior Software Engineer/consultant (Paris), an NVIDIA Deep Learning Institute Instructor (Morocco), and an Infrastructure Engineer at IBM (Dublin).Here's what they actually said:- The Senior Software Engineer said: "LLMs are babies. If you don't understand the architecture behind everything, you won't be able to follow."- The Google advocate pushed back slightl
Still, it got us thinking about how it was ludicrous that while our product has a side effect of decreasing token spend and speeding up agentic workflows by many orders of magnitude, we were using these top tier models for all types of internal coding tasks without any of those optimizations. The waste felt pretty ridiculous — the most glaring culprit was that we were seemingly using the max intelligence model on max reasoning for every task even when the task clearly didn’t require it. As a com
Hi HN!<p>I'm building Jin - an open-source protocol for agent builders and webmasters to make the everyday internet legible for AI agents.<p>I use AI agents for coding & research and one thing I've observed is that we spend to much time, money or both on giving scraping powers to our agents. That got me thinking, what if there was a system for my AI agent to deliver what I want from the internet without having to break a sweat? And that gave me the intent layer idea.<p>Here's how Jin works - 1. AI agent side: you only pass the instructions to your agent in a skill.json or skill.xml format. That's the first step. The skill will tell the agent to look for jin.json on the website and use the intent map to navigate, take action like CRUD, etc. It lives under app/.well-known/jin.json<p>2. Webmaster side: the webmasters can run the jin-cli at the root of their project, the cli will map the project, generate intent map in a jin.json file and save it wherever the app lives. For example, in a monolithic structure, the jin.json can live at the root. For a monorepo structure, it can live under app/web or app/marketing, etc. This file can be totally edited the way you want. Apart from generating intent maps, it tells you how you can easily edit, what you can put, what should go & what not, etc. There's a to-do list in jin.json<p>Along with this, in the latest version, I've added a Jin shield to protect websites from unauthorised scrapers. This shiled lives next to whatever firewall you are using, consider this like a first line of defence. It will snitch who came in. You can register your agent on meetjin.com for free and use the generated key to access any website that uses Jin shield. The agents carry a cryptographic RS256 JWT passport and the Jin shield verifies this locally using cached JWKS public keys.<p>The specification is open (CC0), the tooling is Apache 2.0. Use it, break it, roast it praise it.<p>Access the cli: npm i @papercargo/jin-cli Access the key: <a href="https://meetjin.com" rel="nofollow">https://meetjin.com</a> Github: <a href="https://github.com/meetjin/jin" rel="nofollow">https://github.com/meetjin/jin</a><p>I would really appreciate to know your use case and feedback.<p>Cheers!