Picture by Ideogram
Most of my days as an information scientist seem like this:
- Stakeholder: “Are you able to inform us how a lot we made in promoting income within the final month and what number of that got here from search adverts?”
- Me: “Run an SQL question to extract the information and hand it to them.”
- Stakeholder: “I see. What’s our income forecast for the subsequent 3 years?”
- Me: “Consolidate information from a number of sources, communicate to the finance crew, and construct a mannequin that forecasts income.”
Duties just like the above are advert hoc requests from enterprise stakeholders. They take round 3–5 hours to finish and are often unrelated to the core venture I am engaged on.
When data-related questions like these are available in, they usually require me to push the deadlines of present initiatives or work additional hours to get the job accomplished. And that is the place AI is available in.
As soon as AI fashions like ChatGPT and Claude had been made accessible, the crew’s effectivity improved, as did my potential to reply to advert hoc stakeholder requests. AI dramatically decreased the time I spent writing code, producing SQL queries, and even collaborating with totally different groups for required info. Moreover, after AI code assistants like Cursor had been built-in with our codebases, effectivity positive factors improved even additional. Duties just like the one I simply defined above might now be accomplished twice as quick as earlier than.
Lately, when MCP servers began gaining recognition, I assumed to myself:
Can I construct an MCP that automates these information science workflows additional?
I spent two days constructing this MCP server, and on this article, I’ll break down:
- The outcomes and the way a lot time I’ve saved with my information science MCP
- Sources and reference supplies used to create the MCP
- The fundamental setup, APIs, and companies I built-in into my workflow
# Constructing a Information Science MCP
In the event you do not already know what an MCP is, it stands for Mannequin Context Protocol and is a framework that permits you to join a big language mannequin to exterior companies.
This video is a superb introduction to MCPs.
// The Core Drawback
The issue I wished to unravel with my new information science MCP was:
How do I consolidate info that’s scattered throughout numerous sources and generate outcomes that may instantly be utilized by stakeholders and crew members?
To perform this, I constructed an MCP with three elements, as proven within the flowchart beneath:
Picture by Writer | Mermaid
// Element 1: Question Financial institution Integration
As a information base for my MCP, I used my crew’s question financial institution (which contained questions, a pattern question to reply the query, and a few context concerning the tables).
When a stakeholder asks me a query like this:
What share of promoting income got here from search adverts?
I now not must look via a number of tables and column names to generate a question. The MCP as a substitute searches the question financial institution for the same query. It then positive factors context concerning the related tables it ought to question and adapts these queries to my particular query. All I must do is name the MCP server, paste in my stakeholder’s request, and I get a related question in a couple of minutes.
// Element 2: Google Drive Integration
Product documentation is often saved in Google Drive—whether or not it is a slide deck, doc, or spreadsheet.
I linked my MCP server to the crew’s Google Drive so it had entry to all our documentation throughout dozens of initiatives. This helps shortly extract information and reply questions like:
Are you able to inform us how a lot we made in promoting income within the final month?
I additionally listed these paperwork to extract particular key phrases and titles, so the MCP merely has to undergo the key phrase checklist based mostly on the question quite than accessing tons of of pages directly.
For instance, if somebody asks a query associated to “cell video adverts,” the MCP will first search via the doc index to determine probably the most related recordsdata earlier than wanting via them.
// Element 3: Native Doc Entry
That is the only part of the MCP, the place I’ve an area folder that the MCP searches via. I add or take away recordsdata as wanted, permitting me so as to add my very own context, info, and directions on prime of my crew’s initiatives.
# Abstract: How My Information Science MCP Works
Here is an instance of how my MCP at present works to reply advert hoc information requests:
- A query is available in: ”What number of video advert impressions did we serve in Q3, and the way a lot advert demand do now we have relative to produce?”
- The doc retrieval MCP searches our venture folder for “Q3,” “video,” “advert,” “demand,” and “provide,” and finds related venture paperwork
- It then retrieves particular particulars concerning the Q3 video advert marketing campaign, its provide, and demand from crew paperwork
- It searches the question financial institution for related questions on advert serves
- It makes use of the context obtained from the paperwork and question financial institution to generate an SQL question about Q3’s video marketing campaign
- Lastly, the question is handed to a separate MCP that’s linked to Presto SQL, which is routinely executed
- I then collect the outcomes, assessment them, and ship them to my stakeholders
# Implementation Particulars
Right here is how I applied this MCP:
// Step 1: Cursor Set up
I used Cursor as my MCP shopper. You’ll be able to set up Cursor from this hyperlink. It’s primarily an AI code editor that may entry your codebase and use it to generate or modify code.
// Step 2: Google Drive Credentials
Virtually all of the paperwork utilized by this MCP (together with the question financial institution) had been saved in Google Drive.
To offer your MCP entry to Google Drive, Sheets, and Docs, you will must arrange API entry:
- Go to the Google Cloud Console and create a brand new venture.
- Allow the next APIs: Google Drive, Google Sheets, Google Docs.
- Create credentials (OAuth 2.0 shopper ID) and save them in a file known as
credentials.json.
// Step 3: Set Up FastMCP
FastMCP is an open-source Python framework used to construct MCP servers. I adopted this tutorial to construct my first MCP server utilizing FastMCP.
(Notice: This tutorial makes use of Claude Desktop because the MCP shopper, however the steps are relevant to Cursor or any AI code editor of your selection.)
With FastMCP, you may create the MCP server with Google integration (pattern code snippet beneath):
@mcp.instrument()
def search_team_docs(question: str) -> str:
"""Search crew paperwork in Google Drive"""
drive_service, _ = get_google_services()
# Your search logic right here
return f"Trying to find: {question}"
// Step 4: Configure the MCP
As soon as your MCP is constructed, you may configure it in Cursor. This may be accomplished by navigating to Cursor’s Settings window → Options → Mannequin Context Protocol. Right here, you will see a piece the place you may add an MCP server. If you click on on it, a file known as mcp.json will open, the place you may embody the configuration in your new MCP server.
That is an instance of what your configuration ought to seem like:
{
"mcpServers": {
"team-data-assistant": {
"command": "python",
"args": ["path/to/team_data_server.py"],
"env": {
"GOOGLE_APPLICATION_CREDENTIALS": "path/to/credentials.json"
}
}
}
}
After saving your modifications to the JSON file, you may allow this MCP and begin utilizing it inside Cursor.
# Last Ideas
This MCP server was a easy aspect venture I made a decision to construct to avoid wasting time on my private information science workflows. It is not groundbreaking, however this instrument solves my rapid ache level: spending hours answering advert hoc information requests that take away from the core initiatives I am engaged on. I consider {that a} instrument like this merely scratches the floor of what is potential with generative AI and represents a broader shift in how information science work will get accomplished.
The normal information science workflow is shifting away from:
- Spending hours discovering information
- Writing code
- Constructing fashions
The main target is shifting away from hands-on technical work, and information scientists are actually anticipated to have a look at the larger image and clear up enterprise issues. In some instances, we’re anticipated to supervise product selections and step in as a product or venture supervisor.
As AI continues to evolve, I consider that the strains between technical roles will grow to be blurred. What’s going to stay related is the talent of understanding enterprise context, asking the best questions, decoding outcomes, and speaking insights. If you’re an information scientist (or an aspiring one), there is no such thing as a query that AI will change the best way you’re employed.
You’ve gotten two decisions: you may both undertake AI instruments and construct options that form this transformation in your crew, or let others construct them for you.
Natassha Selvaraj is a self-taught information scientist with a ardour for writing. Natassha writes on all the things information science-related, a real grasp of all information matters. You’ll be able to join along with her on LinkedIn or take a look at her YouTube channel.
Picture by Ideogram
Most of my days as an information scientist seem like this:
- Stakeholder: “Are you able to inform us how a lot we made in promoting income within the final month and what number of that got here from search adverts?”
- Me: “Run an SQL question to extract the information and hand it to them.”
- Stakeholder: “I see. What’s our income forecast for the subsequent 3 years?”
- Me: “Consolidate information from a number of sources, communicate to the finance crew, and construct a mannequin that forecasts income.”
Duties just like the above are advert hoc requests from enterprise stakeholders. They take round 3–5 hours to finish and are often unrelated to the core venture I am engaged on.
When data-related questions like these are available in, they usually require me to push the deadlines of present initiatives or work additional hours to get the job accomplished. And that is the place AI is available in.
As soon as AI fashions like ChatGPT and Claude had been made accessible, the crew’s effectivity improved, as did my potential to reply to advert hoc stakeholder requests. AI dramatically decreased the time I spent writing code, producing SQL queries, and even collaborating with totally different groups for required info. Moreover, after AI code assistants like Cursor had been built-in with our codebases, effectivity positive factors improved even additional. Duties just like the one I simply defined above might now be accomplished twice as quick as earlier than.
Lately, when MCP servers began gaining recognition, I assumed to myself:
Can I construct an MCP that automates these information science workflows additional?
I spent two days constructing this MCP server, and on this article, I’ll break down:
- The outcomes and the way a lot time I’ve saved with my information science MCP
- Sources and reference supplies used to create the MCP
- The fundamental setup, APIs, and companies I built-in into my workflow
# Constructing a Information Science MCP
In the event you do not already know what an MCP is, it stands for Mannequin Context Protocol and is a framework that permits you to join a big language mannequin to exterior companies.
This video is a superb introduction to MCPs.
// The Core Drawback
The issue I wished to unravel with my new information science MCP was:
How do I consolidate info that’s scattered throughout numerous sources and generate outcomes that may instantly be utilized by stakeholders and crew members?
To perform this, I constructed an MCP with three elements, as proven within the flowchart beneath:
Picture by Writer | Mermaid
// Element 1: Question Financial institution Integration
As a information base for my MCP, I used my crew’s question financial institution (which contained questions, a pattern question to reply the query, and a few context concerning the tables).
When a stakeholder asks me a query like this:
What share of promoting income got here from search adverts?
I now not must look via a number of tables and column names to generate a question. The MCP as a substitute searches the question financial institution for the same query. It then positive factors context concerning the related tables it ought to question and adapts these queries to my particular query. All I must do is name the MCP server, paste in my stakeholder’s request, and I get a related question in a couple of minutes.
// Element 2: Google Drive Integration
Product documentation is often saved in Google Drive—whether or not it is a slide deck, doc, or spreadsheet.
I linked my MCP server to the crew’s Google Drive so it had entry to all our documentation throughout dozens of initiatives. This helps shortly extract information and reply questions like:
Are you able to inform us how a lot we made in promoting income within the final month?
I additionally listed these paperwork to extract particular key phrases and titles, so the MCP merely has to undergo the key phrase checklist based mostly on the question quite than accessing tons of of pages directly.
For instance, if somebody asks a query associated to “cell video adverts,” the MCP will first search via the doc index to determine probably the most related recordsdata earlier than wanting via them.
// Element 3: Native Doc Entry
That is the only part of the MCP, the place I’ve an area folder that the MCP searches via. I add or take away recordsdata as wanted, permitting me so as to add my very own context, info, and directions on prime of my crew’s initiatives.
# Abstract: How My Information Science MCP Works
Here is an instance of how my MCP at present works to reply advert hoc information requests:
- A query is available in: ”What number of video advert impressions did we serve in Q3, and the way a lot advert demand do now we have relative to produce?”
- The doc retrieval MCP searches our venture folder for “Q3,” “video,” “advert,” “demand,” and “provide,” and finds related venture paperwork
- It then retrieves particular particulars concerning the Q3 video advert marketing campaign, its provide, and demand from crew paperwork
- It searches the question financial institution for related questions on advert serves
- It makes use of the context obtained from the paperwork and question financial institution to generate an SQL question about Q3’s video marketing campaign
- Lastly, the question is handed to a separate MCP that’s linked to Presto SQL, which is routinely executed
- I then collect the outcomes, assessment them, and ship them to my stakeholders
# Implementation Particulars
Right here is how I applied this MCP:
// Step 1: Cursor Set up
I used Cursor as my MCP shopper. You’ll be able to set up Cursor from this hyperlink. It’s primarily an AI code editor that may entry your codebase and use it to generate or modify code.
// Step 2: Google Drive Credentials
Virtually all of the paperwork utilized by this MCP (together with the question financial institution) had been saved in Google Drive.
To offer your MCP entry to Google Drive, Sheets, and Docs, you will must arrange API entry:
- Go to the Google Cloud Console and create a brand new venture.
- Allow the next APIs: Google Drive, Google Sheets, Google Docs.
- Create credentials (OAuth 2.0 shopper ID) and save them in a file known as
credentials.json.
// Step 3: Set Up FastMCP
FastMCP is an open-source Python framework used to construct MCP servers. I adopted this tutorial to construct my first MCP server utilizing FastMCP.
(Notice: This tutorial makes use of Claude Desktop because the MCP shopper, however the steps are relevant to Cursor or any AI code editor of your selection.)
With FastMCP, you may create the MCP server with Google integration (pattern code snippet beneath):
@mcp.instrument()
def search_team_docs(question: str) -> str:
"""Search crew paperwork in Google Drive"""
drive_service, _ = get_google_services()
# Your search logic right here
return f"Trying to find: {question}"
// Step 4: Configure the MCP
As soon as your MCP is constructed, you may configure it in Cursor. This may be accomplished by navigating to Cursor’s Settings window → Options → Mannequin Context Protocol. Right here, you will see a piece the place you may add an MCP server. If you click on on it, a file known as mcp.json will open, the place you may embody the configuration in your new MCP server.
That is an instance of what your configuration ought to seem like:
{
"mcpServers": {
"team-data-assistant": {
"command": "python",
"args": ["path/to/team_data_server.py"],
"env": {
"GOOGLE_APPLICATION_CREDENTIALS": "path/to/credentials.json"
}
}
}
}
After saving your modifications to the JSON file, you may allow this MCP and begin utilizing it inside Cursor.
# Last Ideas
This MCP server was a easy aspect venture I made a decision to construct to avoid wasting time on my private information science workflows. It is not groundbreaking, however this instrument solves my rapid ache level: spending hours answering advert hoc information requests that take away from the core initiatives I am engaged on. I consider {that a} instrument like this merely scratches the floor of what is potential with generative AI and represents a broader shift in how information science work will get accomplished.
The normal information science workflow is shifting away from:
- Spending hours discovering information
- Writing code
- Constructing fashions
The main target is shifting away from hands-on technical work, and information scientists are actually anticipated to have a look at the larger image and clear up enterprise issues. In some instances, we’re anticipated to supervise product selections and step in as a product or venture supervisor.
As AI continues to evolve, I consider that the strains between technical roles will grow to be blurred. What’s going to stay related is the talent of understanding enterprise context, asking the best questions, decoding outcomes, and speaking insights. If you’re an information scientist (or an aspiring one), there is no such thing as a query that AI will change the best way you’re employed.
You’ve gotten two decisions: you may both undertake AI instruments and construct options that form this transformation in your crew, or let others construct them for you.
Natassha Selvaraj is a self-taught information scientist with a ardour for writing. Natassha writes on all the things information science-related, a real grasp of all information matters. You’ll be able to join along with her on LinkedIn or take a look at her YouTube channel.
