Both have an official MCP. They are reachable differently.
This is an unusually close comparison, because Tana is one of the few note tools with an official, vendor-built MCP server. It supports read and write: an assistant can search nodes, read them, and run mutations like tagging or setting field content. If you want a structured outliner with a real MCP, Tana is a serious option.
The difference is where the server lives. Tana's MCP runs locally, on your machine, while the desktop app is open. Hjarni's MCP is hosted at a remote endpoint. That single fact drives most of the practical tradeoffs below.
Local-first versus hosted
A local MCP server is great when you are working at your own desk with the app running. Your data stays on your machine, and the connection is direct. The cost is reachability: a cloud assistant cannot call a server that only exists on your laptop, and nothing responds when the app is closed or you are on another device.
Hjarni runs the server for you. Because the endpoint is hosted, Claude, ChatGPT, and other MCP clients can reach your notes from any device, with no app to keep open. The tradeoff is the mirror image of Tana's: less local control, more always-on reach.
Tana's MCP is local-first and structured. Hjarni's is hosted and always reachable. Neither is wrong; they optimize for different setups.
Structured nodes versus plain Markdown
The other real difference is the data model. In Tana, everything is a node, and supertags turn nodes into typed objects with fields and views, closer to a database over an outline. That structure is powerful if you want to model your knowledge precisely.
Hjarni is plainer on purpose: Markdown notes in folders, with folder-level AI instructions. There is less structure to maintain, and the notes stay simple text you own. Which is better depends on whether you want a typed outliner or a clean note store.
The learning curve is the real cost of Tana's power
The supertag model that makes Tana strong is also its steepest tax. Reviewers widely describe ten to fifteen hours of upfront setup before the system pays off, and more than a few say the modeling work "feels like coding." Users tend to split into two camps: the ones who invested the time and stayed, and the ones who found it too complicated and left. None of that is a knock on Tana's mobile app, which now does inline editing too. It is about how much system you have to build before the tool starts helping.
Hjarni asks for none of that upfront work. There is no schema to design, no fields to type, no views to configure. You write plain Markdown, and your own ChatGPT or Claude does the organizing through MCP. The contrast is not that Tana's structure is wrong; it rewards people who want to model their knowledge precisely. It is that Hjarni gives you value on the first note, with no system-building tax to pay first.
When Tana is the better fit
If you want a structured, database-like outliner with supertags and typed fields, and you are comfortable keeping the desktop app open for its local MCP, Tana is an excellent choice. Power users who think in nodes and views often prefer exactly that.
When teams pick Hjarni instead
The case for Hjarni is reach and simplicity. A hosted MCP means your notes answer from any device and from cloud AI without running an app, and plain Markdown means less structure to maintain. If those matter more than typed fields, Hjarni fits better.
Hjarni notes are plain Markdown you own, exportable as a ZIP anytime, hosted in the EU.