Your mcp server is not just limited to one single vector database, of course you can include more.
And especially if you want to talk about various different things and you have various different
knowledge sources, of course you can simply include more vector databases. Now I already have
a vector database in Bindcom and it's called Scrape and I have basically just scraped some
information about prompting. The prompting information that I have included is here,
this chain of draft prompting I have already showed to this PDF a few times, but I do think
this is a great information source. It is relatively new, it is from 2025 and most LLMs are not
trained on this data and I do think that an LLM should know this training data, this training
technique and if you want to talk about prompting with an LLM it can make sense that you include
this data. Of course if you have your own company you can include here various different data sources,
maybe one apartment about sales, the other apartment about marketing, the other apartment
about product and you can simply make different vector databases with different sources. This
makes more sense to use different databases because it is in a better structure. If you throw
every single thing in one single vector database it can make mistakes. And this will be a quick video
because it's really easy. Because everything is set up you simply press plus, we use once again
the bindcom vector store. Of course we need to give it a name, this time let's just call it
prompting. We copy this, the name of this knowledge let's just say prompting underscore info, use this
tool for info about prompt engineering. Of course you can also read everything
like in small letters, this is just from my german background, I normally speak german.
Then the bindcom index is this time of course scrape I have told you. Then the limit so the
topk result we still leave it at 4 you can play with this a bit but 4 is a general rule of thumb
that works always great. And then of course we need to give our namespace and the namespace in
this thing is of course prompting so we copy these to make no mistakes and we include this namespace
and then we are basically done, we rename it and boom. And what we need to do right now is of course
once again to use an embeddings model, the same one that we used to embed this document I have
used the text embeddings free small, you already know how to create this vector databases so I
will not waste your time and boom. Right now we press save and you can also ask stuff about
prompting or about the chain of draft prompting inside of N8N so for example what is COD prompting
and normally an LLM is not trained on this data but because our agent will talk to the client,
the client to the server, the server will search this knowledge, it will absolutely know what
chain of draft prompting is. A normal LLM has most likely no info about this but you can include
as you know every single concept that you want. And boom we have an error and I hope you see it.
My prompt was of course what is COD prompting and the answer is not right. This tool tells me that
COD prompting stands for chain of thought decomposition prompting and this is not right
and you maybe also see that we have not triggered our mcb client tool and this is now a great chance
in order to improve our system prompt to get the right answers back because sometimes as soon as
these tools get too big as soon as we use a lot of stuff on our server it can happen that the agent
no longer understands that it needs to use the mcb client for our new information. It's also important
to press save from time to time maybe I have also forgotten to press the save button. I have deleted
my output here and we get also an error. Why we do get this error I will tell you if we come to
executions you see that we did not search our pdf. So basically what we do is to come into the agent
and simply tell the agent if you got questions about COD prompting use the mcb client and now
we ask once again let's just see if this is working right now what is COD prompting. Right now
we trigger the client the client will now talk hopefully with the server the server will search
the prompting vector database and we will get back that COD prompting is of course the chain of
draft prompting. In our previous conversation we got the wrong information back and right now boom
we are done it is working just perfectly. So you see it is the chain of draft prompting and if we
come on executions right now let's just see the newest one you see that we used our vector database
for our information. In our previous conversation we got something back that was not right you see it
did not use to the mcb server and not our vector database and the question was once again what is
COD prompting and the thing thought that COD prompting stands for chain of thought decomposition
prompting so basically this was absolutely wrong but now after an update in the system prompt we get
of course the right answer back. COD prompting stands for chain of draft prompting and I hope that I
don't have to tell you that if you come back into cursor reload it you also have here your prompting
info and if you restart clot and we wait until our N8N mcb server comes through there it is we also
have here the prompting info so I do think this is really powerful you can connect more than just
one vector database to your mcb server and you can split out everything just perfectly fine and you
will get the right information back as easy as possible. This is a powerful concept if you have
different apartments in your company if you want to do specific stuff with different vector databases
you should absolutely include different vector databases.