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Winter is coming [music] and for you
there in the Alps that means Avalanche
safety is top of mind, right Kristoff?
And you told me there is an MCP server
for that.
>> Yes, people rely on LLM responses more
and more when it comes to planning out
their adventures. The data and
information need to be accurate. Let me
show you how I set up this MCP server.
Welcome to the show, Kristoff. What do
you do here at Google?
>> Thanks for having me, Martin. I'm
currently doing a rotation as a software
engineer in the Cloudr run team, working
on the user experience, but I also work
with the largest customers on Google
Cloud to bring their serverless
applications up and running. Okay, now
show me this Avalanche MCP server.
>> Someone might build the next generation
ski body AI app which can answer your
questions about skiing and also inform
you about the current avalent risk. Such
agent can connect to MCP servers to
fetch this up-to-date data. For the
purpose of this demo, I've connected my
Gemini CLI to the URL of the Avalanche
Warning MCP server, which runs on Google
Cloud Run. But think of any app or
agent. I'm going to ask here, what is
the current avalanche danger level at
Mountain Solstein?
Tell me what the snow pack is like.
>> Okay, it's thinking. Look at that. It
asked me for permission to use those
tools.
Found the region ID for the mountain
solinine. Put the current data for this
region and got us a grounded answer
based on the official records. No
hallucinations.
>> All right. Now, why do we need an MCP
server for avalanche data?
>> It is actually a perfect use case. So
the model context protocol or MCP is
basically a standardized way to plug in
real time data into a large language
model. An LLM would not intrinsically
know about most up-to-date data.
>> Okay. So it's like an open API spec but
optimized for AI.
>> Exactly. Standard APIs are messy for AI.
You have to write good prompts and code
to parse the model output to explicitly
execute some API requests. MCP on the
other hand is self-describing.
>> How is it self-describing?
>> The MCP server tells the LLM,
hey, I have a tool to check snow
conditions and explains
how to use it in plain English. Here is
a list of tools of the MCP Avalanche
server. Each written with a description
and a JSON schema defining tools input.
>> Got it. Uh but usually I see people
running uh these MCP servers locally on
their laptops. Uh why put this in the
cloud?
>> Uh great question. Um locally is fine
for personal offline tools like reading
from your own file system.
But an avalanche warning service wants
to provide this endpoint directly so
users don't need to install the MCP
somewhere but just can connect to a
public URL
>> and why use cloud run for your MCP
server in the cloud
>> for avalanch warning service MCP server
projects cloud run is a perfect match.
Think about it. The traffic is spiky as
a lot of people checked the Avalanche
report
on a day when it snows and they got
prepared for the next day's outdoor
adventure. But almost nobody is quing
this endpoint at 3:00 a.m. in the
summer.
>> And you don't want to pay for a server
that's sitting idle all night. Right.
>> Exactly. Cloudr run scales to zero. So
if no one is asking about the
avalanches, almost nothing is getting
charged. But if a blizzard hits or
champagne powder falls, traffic will
spike suddenly. Cloudr run will scale up
automatically.
>> I love that champagne powder. Uh okay,
so that sounds efficient with the
scaling. Now, how does the AI actually
get the right avalanche data?
>> Sure. Here is the Avalanche MCP server
code by the Austrian Avalanche warning
service. We expose two tools. One
which is called get region ID. You give
it a mountain name and it gives you back
this particular ID.
And a second tool you get the bulletine
where you can give it this particular ID
plus a specific date and optionally a
language and it gives you back the
danger rating and other related data in
a structured way because they are
clearly described in MCP. The large
language model figures out the logic on
its own and it's chaining those two
tools together.
>> Okay, I I think I understand how that
works. How would you deploy this MCP
server to Cloud Run?
>> That is simple. We don't need to touch
any server or clusters. I just run run a
single command to deploy this code as a
cloud run service. That command takes my
code
builds a container out of it and
provides me back with an URL and handles
all the scaling rules we talked about.
>> Very good. Uh, let's take a tea or
coffee break while it's running.
>> I will pack my backpack to get ready for
later the good snow.
And now it's back. Here is the URL of my
newly deployed MCP server. I can give
this URL to Gemini CLI or any AI agent.
Someone out there built and want to rely
on the Avalanche data.
>> Very good. Now, let's recap. What are
the main takeaways here, Kristoff?
>> I have two. MCP is useful for connecting
AI apps and agents to real time data
like fetching avalanche warning data.
Second, Cloudr run is a great place to
run your MCP servers because it handles
all the MCP requests in a serverless way
and autoscales up and down
automatically.
>> Great takeaways and thank you for
joining me today Kristoff.
>> Thanks Martin for having me
>> and thank you everyone for watching. If
you have any questions for Kristoff or
me, ask in the comments and do let me
know what you thought of today's
episode.
I read every single comment. I can't
wait to see what you build.
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Tutorial ā https://goo.gle/4s5mwJd Avalanche MCP server source code ā https://goo.gle/48FE07i Christoph Stanger joins Martin Omander to demostrate a production grade Avalanche warning MCP server running on Cloud Run. Join the duo as they dive into the architecture of exposing real time data to large language models, showing how to leverage Cloud Run's serverless platform to get a secure public endpoint and automatic scaling for your AI tools. Chapters: 0:00 - Intro 0:56 - The avalanche MCP server 1:30 - [Demo] 2:01 - Why MCP? 3:50 - Why MCP on Cloud Run? 4:52 - Code 5:40 - Deploying an MCP server to Cloud Run 6:40 - Takeaways 7:14 - Summary Watch more Serverless Expeditions ā https://goo.gle/ServerlessExpeditions š Subscribe to Google Cloud Tech ā https://goo.gle/GoogleCloudTech #Serverless #GoogleCloud Speakers: Martin Omander, Christoph Stanger Products Mentioned: Cloud Run