Mirror.xyz MCP. Access decentralized Web3 articles via prompt.

Mirror.xyz connects your agent straight into decentralized content on Arweave and ENS domains. You'll use this server when you need your AI client to handle web3 data, bypassing traditional search engines or centralized APIs. It provides two core functions for deep content retrieval: one tool lists publications using an ENS name, and the other pulls the complete article details from a specific Arweave digest.

When you run get_entries, you hand it an ENS name—say, yourproject.eth—and it returns a clean list of every publication that's been posted under that Mirror profile. This isn't just metadata; it's the working index for all available content associated with that specific decentralized identity. You get a bulletproof log of links and timestamps showing you exactly what was published, letting your agent track project timelines or editorial history for any given Web3 entity.

If you know which article you want, get_entry is your play. It takes the unique Arweave digest—that long string of characters pointing to the data block—and pulls back everything attached to that single post. You don't just get the text; you get the full content body, the title, and all the metadata about the entry, including who authored it and when.

This is crucial because knowing the author isn't enough; you need the context of the submission itself.

Your agent can use these mechanisms to analyze web3 data streams by pulling multiple pieces of information. For example, if a project publishes a governance proposal on Mirror, your client uses get_entry with the digest to pull the full text of that proposal, and then it might run get_entries first to see when the proposal was published relative to other announcements.

You can build complex research flows—the agent pulls a list via ENS name, picks the most relevant link from that list, and then uses the Arweave digest on that link to get all the details needed for a summary or analysis.

Think about it: you're not manually checking block explorers anymore. Your client acts like a dedicated web3 librarian, querying the source of truth directly. If you need to summarize project updates across several months of decentralized journalism, your agent doesn't guess; it gets the raw data points for every entry needed.

When analyzing governance proposals or market commentary published on Mirror.xyz, you can feed the complete text from get_entry into your AI client. You don't have to sift through boilerplate language. The tool gives you the actual words and the associated author details, so you're working with verifiable primary source material.

This capability means you can build logic that tracks shifts in sentiment or changes in technical focus across a series of posts—it’s all about the data flow.

It’s simple: give your agent an ENS name to see what’s out there, or give it a digest to read exactly what was said. You'll use these tools to build workflows that summarize project activity or analyze complex discussions by pulling structured lists and then deep-diving into the full text of each piece of content retrieved from Arweave.