Alternatives
Products that do what MCP agents handle an incident workflow [video] does
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I'm building Incidental, an open-source (MIT license) incident management platform. I've been working on it for the past couple of months as a hobby, and now it's at a state where I'm comfortable sharing it. This is also my first open source project. Features: - Custom roles - Custom severities - Integrated with Slack - Web interface Todos: - Custom fields - Custom workflows Website: https://incidental.dev Github: https://github.com/incidentalhq/incidental I'd love to hear your feedback. Thanks!
2024 · github.com
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Hello, HN. I've created fast-agent to make building my own products easier - and remove the friction between defining Prompts, MCP Servers and their composition. It uses a simple, declarative style that's easy to work with and source control - with inbuilt support for the patterns in the Building Effective Agents paper. Because you can "warm-up" and interact with Agents before, during or after the workflows, it's easy to diagnose and tune Agent prompts and behaviour for later runs. Being able to set these workflows up makes LLM Context Management and Tool Selection a lot easier and can…
2025 · github.com
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We wanted to build a course for new Mastra devs to get started quickly. However, we knew videos would go out of date and be more difficult to maintain. We decided to launch our "course" as an MCP server. This way your coding agent actually teaches the course content to you and can help you write the code. We think this is a really interactive way to learn. Using an editor with MCP support (such as Cursor, Windsurf, or VSCode), your code agent will call the appropriate MCP tools which will return context for the agent. This context tries to instruct the agent that it should be teaching you…
2025 · mastra.ai
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Hey HN, I spent my xmas break building an agent framework called mcp-agent [1](https://github.com/lastmile-ai/mcp-agent) for Model Context Protocol [2]. It makes it easy to build AI apps with MCP servers, and implements every pattern from the popular Building Effective Agents blog [3] as well as OpenAI’s Swarm [4]. I’m sharing it early to get community feedback on where to take it from here, and to ask for contributions. For those who aren’t familiar with MCP, I think of it as a standardized interface to let AI communicate with software via tool calls, resources and…
2025 · github.com
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2021 · workflow86.com
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Hey HN! We’ve been building an MCP server to help AI-assisted web app developers by using browser agents to test whether changes made by an AI inside an editor actually work. We've been testing it on scenarios like verifying new flows in a UI, or checking that sending a chat request triggers a response. The idea is to let your coding agent both code and evaluate if what it did was correct. Here’s a short demo with Cursor: https://www.youtube.com/watch?v=_AoQK-bwR0w When building apps, we found the hardest part of AI-assisted coding isn’t the coding—it’s tedious point-and-click…
2025 · github.com
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Hey HN! A few months ago we shared mcp-agent (https://github.com/lastmile-ai/mcp-agent) [1][2], a lightweight framework that implements every agent pattern from Anthropic’s Building Effective Agents blog [3] and handles MCP server/client management seamlessly. Our core bet is that connecting LLMs to tools, resources, and external systems will soon be MCP-native by default. Today we're launching a significant update: Agents as MCP servers. Currently "agentic" behavior exists only on the MCP client side – clients like Claude or Cursor use MCP servers to solve tasks.…
2025 · github.com
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Hey HN – Gregor & Magnus here again. A few months ago, we launched Browser Use (https://news.ycombinator.com/item?id=43173378), which let LLMs perform tasks in the browser using natural language prompts. It was great for one-off tasks like booking flights or finding products—but we soon realized enterprises have somewhat different needs: They typically have one workflow with dynamic variables (e.g., filling out a form and downloading a PDF) that they want to reliably run a million times without breaking. Pure LLM agents were slow, expensive, and unpredictable for these…
2025 · github.com
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