Bad data quickly erodes GP and LP trust: whether that’s net burn reported as a positive number, ARR reported differently than the prior quarter, or quarterly figures that don’t tie out to annual totals. An MCP-powered data quality auditor can automate this review by running a standard set of questions against Standard Metrics and returning a triage list in minutes. A working version might look for period-over-period anomalies, areas where figures don’t tie out, or metrics that should be positive or negative. Finance and data teams still own the cleanup, but the tool gives them a data triage queue and helps prevent obvious issues from reaching Partners, LPs, or auditors.
How to set this up
Tools
- Codex
- Standard Metrics MCP. See here for set up details.
- Gmail connector
Prompt used in the video:
45 days after every quarter, analyze AirChair’s financials and flag any metrics from the last quarter that don’t tie out or seem anomalous compared to prior quarters. Do this for every company in my portfolio, aligned with their FYE, and send me an email summarizing all the data problems to investigate.
Tips
- Adjust the lag window (30, 45, 60 days post-quarter) based on when your portfolio companies typically have all their data submitted.
- Calibrate sensitivity by giving the prompt examples of what counts as “anomalous” (e.g. a >25% period-over-period swing in headcount).
- Treat the email as a triage queue, not a verdict. The workflow surfaces data for review; your team still owns the cleanup and the call on what’s a real problem versus a reporting quirk.
- Just set this up for certain companies for a more rapid, token-efficient data quality check
