Sure, absolutely. Before I do, let me just say, this tooling took a lot of work and problem solving to establish in the enterprise, and it's still far from perfect. MCPs are extremely useful IMO, but there are a lot of bad MCP servers out there and even good ones are NOT easy to integrate into a corporate context. So I'm certainly not surprised when I hear about frustrations. I'm far from an LLM hype man myself.
Anyway: a lot of earlier stages of drug discovery involve pulling in lots of public datasets, scouring scientific literature for information related to a molecule, a protein, a disease, etc. You join that with your own data and laboratory capabilities and commercial strategy in order to spot opportunities for new drugs that you could maybe, one day, take into the clinic. This is traditionally an extremely time consuming and bias prone activity, and whole startups have gone up around trying to make it easier.
A lot of the public datasets have MCPs someone has put together around someone's REST API. (For example, a while ago Anthropic released "Claude for Life Sciences" which was just a collection of MCPs they had developed over some popular public resources like PubMed).
For those datasets that don't have open source MCPs, and for our proprietary datasets, we stand up our own MCPs which function as gateways for e.g. running SQL queries or Spark jobs against those datasets. We also include MCPs for writing and running Python scripts using popular bioinformatics libraries, etc. We bundle them with `mcpb` so they can be made into a fully configured one-click installer you can load into desktop LLM clients like Claude Desktop or LibreChat. Then our IT team can provision these fully configured tools for everyone in our organization using MDM tools like Jamf.
We manage the underlying data with classical data engineering patterns, ETL jobs, data definition catalogs, etc, and give MCP-enabled tools to our researchers as front end concierge type tools. And once they find something they like, we also have MCPs which can help transform those queries into new views, ETL scripts, etc and serve them using our non-LLM infra, or save tables, protein renderings, graphs, etc and upload them into docs or spreadsheets to be shared with their peers. Part of the reason we have set it up this way is to work through the limitations of MCPs (e.g. all responses have to go through the context window, so you can't pass large files around or trust that it's not mangling the responses). But also we do this so as to end up with repeatable/predictable data assets instead of LLM-only workflows. After the exploration is done, the idea is you use the artifact, not the LLM, to intact with it (though of course you can interact with the artifact in an LLM-assisted workflow as you iterate once again in developing a yet another derivative artifact).
Some of why this works for us is perhaps unique to the research context where the process of deciding what to do and evaluating what has already been done is a big part of daily work. But I also think there are opportunities in other areas, e.g. SRE workflows pulling logs from Kubernetes pods and comparing to Grafana metrics, saving the result as a new dashboard, and so on.
What these workflows all have in common, IMO, is that there are humans using the LLM as an aid to dive understanding, and then translating that understanding into more traditional, reliable tools. For this reason, I tend to think that the concept of autonomous "agents" is stupid, outside of a few very narrow contexts. That is to say, once you know what you want, you are generally better off with a reliable, predictable, LLM-free application, but LLMs are very useful in the prices of figuring out what you want. And MCPs are helpful there.
