Xops (@xops_ai) profile picture

Build AI workflows in seconds.

Joined February 2025
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Added Claude 4 Sonnet support
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Different types of agents you could build today with xops.net
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Automate Airtable lead calling w/ @airtable => @Vapi_AI! My new video demos importing an existing workflow that filters leads & triggering calls automatically. Heads up: Requires matching Airtable cols (+ Phone column) & using your own Vapi/Airtable IDs (shown in video). If you want this workflow template let me know and I'll DM it. We will have some docs up soon with more example templates people can use or get ideas from to prompt.
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Rolling out some handy improvements for @xops_ai automations ✨ Based on your feedback, we've shipped a few updates this week to make building automations even smoother. Check out what's new 🧵
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1/ Keep your workspace tidy. You can now easily delete workflows 🗑️ you no longer need. Good for clearing out tests or old automations.
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Added tool support for @resend ✉️ Now send emails directly in your automations. Just describe the email (to, from, subject, body) and @xops_ai handles the rest. Here's a peek under the hood at the generated step
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Need to modify an existing Xops workflow? 🤔 Now use the new 'Restore' button functionality 🔄 to load its saved instructions directly into the input box. Makes tweaking your automations much faster!
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Burning the midnight oil over here and shipped @Vapi_AI and @airtable tool support. Connect Voice Agents to your Lead sheets with just a prompt. The next 50 signups get 1000 free credits: xops.net/
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Under the hood xops.net uses OpenRPC A description language for JSON-RPC that lets you define both the params and result schemas. It's already used by many ecosystems. Building on top of OpenRPC shows the real power of planning workflows, agents, and tool calls.
There is a lot of (justified) of excitement about MCP, but just like most function calling approaches, I think it has a severe limitation at the moment, and I am kind of of flabbergasted why I am the only one that is bothered by it. The problem is that when you define a tool using MCP [0] you can only specify the *inputSchema* but not the *outputSchema* (the same is the case for OpenAI [1], Claude [2], Mistral [3], Gemini [4], ...). And the optional description of a tool is just a "hint" to the model. That means that when the model needs to decide to call a tool, it can only rely on the structure of the the inputs (not even on the description). But since it does not know the structure of the output of the call, it cannot reason about the composition of multiple tool calls to solve a complex problem. Instead it needs to take a guess, call some first tool, and then interpret the output before it can reason about how to continue. The fact that tools are dynamically typed wrt to their results makes it also impossible for other tools, and humans, to reason about tools (that is a lot of tools :-). Imagine I would explain MCP to you by only showing the types you send to an MCP server, but none of the types that you receive from a server. Since tools are for all intents and purposes really just fancy REST apis, just like OpenAPI definitions [5], tool calling and MCP tools should also specify their outputs.
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Vibe coding for workflows is here. Xops lets you build AI workflows in seconds. Start now with 1000 free credits xops.net
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Xops ai free trial: 1000 credits to automate your ass off. So good, your to-do list will need a cigarette after. Try it: xops.net @grok
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