Most of the advice aimed at developers right now is about code generation. If you are still copy-pasting snippets out of a chat window, that is the smallest part of what changed. The bigger shift is in the AI data science workflow: tools like the Model Context Protocol (MCP) turn an LLM into something that takes part in your pipeline rather than describing it.
I have watched a lot of side projects die in the WIP stage, because the gap between raw data (a 2GB XML export, say) and anything actionable is too wide to cross in your spare evenings. Lately I have been running end-to-end automation where the AI does not just suggest the Python. It pulls the data out of Google Drive, refactors legacy GitHub repos, and pushes results into BigQuery.
MCP as a universal adapter
The Model Context Protocol (MCP) is worth an afternoon if you have not looked at it yet. It works as a universal adapter for AI applications: the assistant can talk to your local filesystem, your database, or third-party APIs like Slack and GitHub without you writing custom middleware for each one.
In a real AI data science workflow, context does most of the work. Instead of explaining your database schema to Claude for the tenth time, you connect a WordPress MCP server or a Snowflake connector. The AI stops guessing and starts querying.
What the workflow actually looks like
- The AI locates the raw datasets sitting in cloud storage, Google Drive or S3.
- It reads the old GitHub repos to pull out parsing logic that already exists.
- Parsed datasets move into a warehouse like BigQuery.
- Then the analysis loop: run SQL, look at what came back, ask a sharper question.
The 150GB log file
Now the mess, because automation is fine right up until it is not. I saw a case recently where a developer set up an automated AI data science workflow to troubleshoot a BigQuery connection. The AI got stuck in a loop, and by the next morning the machine was reporting 150GB more system data than the night before. The cause was a bigquery-mcp-wrapper.log file, fed by every single failed attempt at a recursive join.
That is the gotcha of the current stack. These magical tools carry a cost, usually as technical debt or disk space, and it lands on you the moment nobody is monitoring them. You still need technical judgment to catch the race conditions before they eat your SSD.
// Example: A basic MCP Server Configuration for a Data Workflow
{
"mcpServers": {
"google-drive": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-google-drive"],
"env": {
"GOOGLE_DRIVE_CREDENTIALS": "/path/to/creds.json"
}
},
"bigquery": {
"command": "python",
"args": ["-m", "mcp_server_bigquery"],
"env": {
"BQ_PROJECT_ID": "my-enterprise-project"
}
}
}
}Domain knowledge is what you bring
There is a lot of fear about AI replacing data scientists. What I notice in practice is narrower: it compresses the time a query takes to write and run, and it still struggles with the why. Show it a drop in user activity in early 2020 and it may land on a lack of motivation. You know it was a global pandemic. That gap is the part you are actually paid for.
This is human-in-the-loop engineering. You are the architect and the reviewer, and the AI handles the data pipelining. For how this plays out in the WordPress ecosystem, there is the WordPress.com Claude Connector and its approach to secure AI integrations.
If this kind of workflow is eating your dev hours, I can take it on. I have been wrestling with WordPress and awkward data integrations since the 4.x days.
What I would take from this
The AI data science workflow is not really about writing code faster. It is about shortening the distance between raw data and analysis somebody can act on. Connect your tools with MCP, and keep auditing the logs while you do it. Left unattended, these wishing machines get expensive. Refactor the process, ship the automation, and keep a hand on the wheel.
