So far I have seen two genuinely good arguments for the use of MCPs:

* They can encapsulate (API) credentials, keeping those out of reach of the model,

* Contrary to APIs, they can change their interface whenever they want and with little consequences.

I started with a UI that sounded like it was built along the same lines as yours, which had the advantage of letting me enforce a pipeline and exhaustivity of search (I don't want the 10 most promising documents, I want all of them).

But I realized I was not using it much because it was that big and inflexible (plus I keep wanting to stamp out all the bugs, which I do not have the time to do on a hobby project). So I ended up extracting it into MCPs (equipped to do full-text search and download OCR from the various databases I care about) and AGENTS.md files (defining pipelines, as well as patterns for both searching behavior and reporting of results). I also put together a sub-agent for translation (cutting away all tools besides reading and writing files, and giving it some document-specific contextual information).

That lets me use Claude Code and Codex CLI (which, anecdotally, I have found to be the better of the two for that kind of work; it seems to deal better with longer inputs produced by searches) as the driver, telling them what I am researching and maybe how I would structure the search, then letting them run in the background before checking their report and steering the search based on that.

It is not perfect (if a search surfaces 300 promising documents, it will not check all of them, and it often misunderstands things due to lacking further context), but I now find myself reaching for it regularly, and I polish out problems one at a time. The next goal is to add more data sources and to maybe unify things further.

Oh! That's a nice use-case and not too far from stuff I have been playing with! (happily I do not have to deal with handwriting, just bad scans of older newspapers and texts)

I can vouch for the fact that LLMs are great at searching in the original language, summarizing key points to let you know whether a document might be of interest, then providing you with a translation where you need one.

The fun part has been build tools to turn Claude code and Codex CLI into capable research assistant for that type of projects.

The paper[0] is actually about their logarithmic number system. Deep learning is given as an example, and their reference implementation is in PyTorch, but it is far from the only application.

Anything involving a large number of multiplications that produce extremely small or extremely large numbers could make use of their number representation.

It builds on existing complex number implementations, making it fairly easy to implement in software and relatively efficient. They provide implementations of a number of common operations, including dot product (building on PyTorch's preexisting, numerically stabilized by experts, log-sum-of-exponentials) and matrix multiplication.

The main downside is that this is a very specialized number system: if you care about things other than chains of multiplications (say... addition?), then you should probably use classical floating-point numbers.

[0]: https://arxiv.org/abs/2510.03426

I have found putting the spec together with a model, having it to try find blindspots and write done the final take in clear and concise language, useful.

A good next step is to have the model provide a detailed step by step plan to implement the spec.

Both steps are best done with a strong planning model like Claude Opus or ChatGPT5, having it write "for my developer", before switching to something like Claude Code.