Dromo

@Dromo show me all uploads from the last 7 days that are still in 'processing' status

Found 4 uploads matching your criteria: 1. customer_contacts_2024.csv (Jan 15, 3:42 PM) — Processing, 2,847 rows detected 2. product_inventory_Q1.xlsx (Jan 14, 9:18 AM) — Processing, 1,203 rows detected 3. vendor_list_updated.csv (Jan 13, 2:31 PM) — Processing, 456 rows detected 4. employee_data_import.csv (Jan 12, 4:55 PM) — Processing, 89 rows detected All four uploads are awaiting validation rules to complete. You can check individual upload details or retry failed rows once processing finishes.

This example shows Dromo's ability to filter and list uploads by status and date range. Useful for monitoring bulk import pipelines. Note that 'processing' status depends on your Dromo validation rules and webhook configuration — uploads may resolve faster or slower based on data quality and your org's settings.

@Dromo upload this customer_leads.csv file to our headless import flow for the sales team

Upload initiated for customer_leads.csv (3.2 MB, 5,614 rows). Dromo returned a signed upload URL and I've transferred the file via PUT request. Upload ID: upl_9x4k2m7n. The file is now queued for validation against your 'Sales Leads' schema. Expected processing time: 2-4 minutes. You'll receive a webhook notification at your configured endpoint once validation completes. Check the Dromo dashboard or use '@Dromo list uploads' to monitor progress.

This demonstrates the two-step headless import flow: obtaining a signed URL from Dromo, then uploading raw file bytes. Requires API key with write permissions. Be aware that large files (>10 MB) may hit rate limits or timeout — consider chunking or using Dromo's batch API for high-volume imports.

@Dromo pull the last 5 completed uploads and summarize any validation errors by column

Analyzed 5 completed uploads (Jan 10-15). Validation errors by column: • **Email** (47 errors across 3 files): Invalid format (32), missing domain (15) • **Phone Number** (23 errors across 2 files): Non-US format (18), missing area code (5) • **Zip Code** (12 errors across 2 files): Invalid length (9), non-numeric (3) • **Company Name** (8 errors across 1 file): Duplicate entries flagged Most common issue: email validation. Consider updating your Dromo schema to allow international email formats or add a pre-upload sanitization step for the 'Email' field.

This example pairs Dromo's upload list with AI reasoning to surface patterns across multiple imports. Helpful for identifying recurring data quality issues. The AI interprets error metadata returned by Dromo — accuracy depends on how your validation rules are configured and whether error details are exposed via the API.

When Dromo makes sense for B2B SaaS customer data ingestion

A 6-person B2B SaaS team handling 15-20 new customer onboardings per month hits the same wall: customers send messy spreadsheets that break the import flow. Dromo's headless import is the right call here if your product already has a data-mapping UI and you just need the upload plumbing. The MCP's two tools let your team programmatically trigger uploads and audit what came through without opening the Dromo dashboard. The threshold: if you're doing fewer than 10 imports per month, the API-key setup overhead probably isn't worth it—just use Dromo's hosted UI. Above that volume, the MCP pays off by letting your support team script common fixes and track upload history in Switchy alongside customer tickets.

Using Dromo MCP to monitor recurring partner file drops

A 3-person ops team at a marketplace company receives weekly CSV exports from 8 partner vendors, each with slightly different schemas. They need to know when a file fails to upload or when a partner changes their column structure without warning. The Dromo MCP's list-uploads tool is built for this: your team can write a daily Switchy prompt that pulls the last 7 days of uploads, flags any missing partners, and surfaces schema drift before it breaks downstream analytics. The trade-off is that Dromo's upload metadata is thin—if you need deep validation logs or row-level error tracking, you'll still need to query your own database. This works best when the upload itself is the signal you care about, not the post-processing outcome.

When Dromo MCP helps legal teams pull historical upload records

A 2-person legal ops team at a fintech startup needs to produce upload audit trails for quarterly compliance reviews. They're using Dromo to ingest customer transaction CSVs, and now they need a repeatable way to export upload metadata without asking engineering for a custom report every quarter. The Dromo MCP's list-uploads tool with pagination support is the right fit: the legal team can run a Switchy prompt once per quarter to pull all uploads in a date range, filter by customer ID, and generate a summary for auditors. The limitation is that Dromo only stores upload metadata, not file contents—if you need to re-download the original CSVs, you'll need a separate storage solution. This MCP is best for teams who need upload provenance, not file archival.