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Comment threads on Hacker News contain some of the most valuable technical discussions on the internet. With the Hacker News MCP server, you can access and navigate these conversations programmatically through simple natural language requests to your AI assistant.
To view comments on a specific story, you first need to identify the story you're interested in. You can ask your assistant to show you top stories, new submissions, or search for discussions on particular topics. Once you've found a relevant story, simply request to see its comments.
For example, you might say: "Show me the comments on the top story about Python" or "What are people saying in the discussion about the latest AI breakthrough?"
The server retrieves the complete comment thread, presenting it in a structured format that preserves the conversation hierarchy. Each comment includes the author's username, the comment text, score (upvotes), timestamp, and information about any replies.
Hacker News comments are organized in a tree structure, where replies appear as children of parent comments. This creates threaded discussions that can branch in multiple directions. When you access a comment thread through the MCP server, this hierarchy is preserved, allowing you to understand the flow of conversation.
You can explore specific branches of a discussion by requesting to see replies to particular comments. Ask questions like "Show me the replies to the top comment" or "What did users say in response to the second comment?" This targeted navigation helps you focus on the most relevant parts of lengthy discussions.
Comment threads often contain expert analysis, alternative perspectives, and technical corrections that add significant value beyond the original submission. You can ask your assistant to summarize discussions, identify common themes, or highlight comments from specific users known for their expertise in a particular domain.
For instance, try requests like "Summarize the main points from this discussion" or "Are there any critical perspectives in these comments?" The server provides the data needed for your AI assistant to analyze and extract meaningful insights from even extensive comment threads.
Each comment has a unique identifier, allowing you to access specific comments directly if you know the ID. This is useful when tracking particular discussions or following up on conversations you've previously reviewed. You can also use the server to access a user's comment history, letting you see all contributions from specific community members.
The Dialer integration lets you make outbound calls, manage call campaigns, and track calling activity directly from your workflow, enabling automated follow-ups and real-time call disposition logging.
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Metorial provides developers with instant access to 600+ MCP servers for building AI agents that can interact with real-world tools and services. Built on MCP, Metorial simplifies agent tool integration by offering pre-configured connections to popular platforms like Google Drive, Slack, GitHub, Notion, and hundreds of other APIs. Our platform supports all major AI agent frameworks—including LangChain, AutoGen, CrewAI, and LangGraph—enabling developers to add tool calling capabilities to their agents in just a few lines of code. By eliminating the need for custom integration code, Metorial helps AI developers move from prototype to production faster while maintaining security and reliability. Whether you're building autonomous research agents, customer service bots, or workflow automation tools, Metorial's MCP server library provides the integrations you need to connect your agents to the real world.