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Accessing and Navigating Comment Threads

Understanding Comment Navigation

Hacker News discussions often contain valuable insights, technical expertise, and diverse perspectives that rival the original submissions in quality. Accessing and navigating these comment threads through the MCP server allows you to extract meaningful information from community discussions efficiently.

Retrieving Comment Threads

To access comments on any Hacker News story, you'll need the story's unique identifier. Once you have this ID, simply ask your AI assistant to retrieve the comments for that specific item. The server will fetch the complete comment thread, including all replies and nested discussions.

Comments are returned in a structured format that preserves the conversation hierarchy. Each comment includes the author's username, the comment text, posting timestamp, score (if available), and references to any replies. This structure makes it easy to understand both individual comments and how they relate to the broader discussion.

Navigating Nested Discussions

Hacker News comments form tree-like structures where users reply to other comments, creating nested conversations. The server preserves this hierarchical relationship, allowing you to follow specific discussion branches that interest you.

When exploring deep comment threads, you can ask your assistant to focus on particular branches or to summarize the main points of discussion. For example, if a story has hundreds of comments, you might request only top-level comments first, then drill down into specific threads that seem relevant to your research.

Accessing Individual Comments

If you know the ID of a specific comment—perhaps referenced elsewhere or found through a direct link—you can retrieve it directly without fetching the entire thread. This is particularly useful when tracking specific discussions or following up on previously identified insights.

Practical Navigation Strategies

For lengthy discussions, consider these approaches:

Progressive Exploration: Start by retrieving top-level comments to get a sense of the overall conversation themes, then navigate into specific threads that warrant deeper investigation.

User-Focused Reading: If you've identified knowledgeable contributors through user profile research, you can specifically look for their comments within threads to find expert perspectives.

Time-Based Review: Access comments in chronological order to understand how discussions evolved, or reverse the order to see the most recent additions to ongoing conversations.

Making the Most of Comment Data

Comment threads become especially valuable when analyzed in context. Consider the story's topic, the authors' backgrounds, and comment scores to gauge the reliability and relevance of different perspectives. The server provides all this contextual information, enabling informed interpretation of community discussions.

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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.

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