How to Fact-Check Data Content Using MCP.

How It Works

Your AI agent receives a draft blog post: 'Remote work increases productivity by 22% according to a Stanford study. Companies that mandate return-to-office are fighting data with ideology.' Phase 1: the agent runs `validate_journalistic_reasoning`.

Source Verification: the Stanford study (Bloom et al., 2015) is real and peer-reviewed. However, it studied call center workers at a Chinese travel agency (CTrip), not knowledge workers broadly.

The claim 'remote work increases productivity by 22%' extrapolates a narrow finding to a universal claim , this is a scope error.

The piece needs a second independent source studying knowledge workers specifically. False Equivalence Check: the draft frames 'remote work supporters' vs.

'return-to-office mandaters' as data vs. ideology. This is false equivalence , there is legitimate evidence on both sides. Microsoft's 2022 Work Trend Index found that remote workers reported lower feelings of connection and mentorship.

The piece must present countervailing evidence honestly. Source Credibility: Stanford study , high credibility (peer-reviewed, reputable institution, named researcher). But it is from 2015 , the remote work landscape has changed fundamentally since then, especially post-pandemic.

The piece should note the temporal gap. Editorial Independence: the draft takes an advocacy position ('fighting data with ideology') while presenting as objective analysis.

This must be either explicitly labeled as opinion or restructured to present evidence without editorial judgment. Verdict: JOURNALISTIC_REASONING_WEAK , scope error, false equivalence, temporal gap, and advocacy framing need correction.

Phase 2: the agent runs `validate_data_analysis`. Statistical Audit: the '22%' figure , the original study measured output per hour for call center workers doing repetitive tasks.

Applying this to knowledge workers doing creative, collaborative work is a category error. Sample size: 249 employees at one company , too small and too narrow for universal claims.

Correlation vs. Causation: even within the study, the productivity increase may be attributed to self-selection bias (workers who volunteered for remote work were likely more motivated).

The study acknowledges this limitation , the draft does not. Visualization Check: no charts in the draft, but the agent flags that if the creator adds a bar chart comparing 'remote' vs.

'office' productivity, the baseline must start at zero to avoid visual distortion. Confidence Interval: the study reports a 95% confidence interval of 18-26%.

The draft cites only the midpoint (22%) without the range, which overstates precision. Verdict: DATA_ANALYSIS_WEAK , category error, self-selection bias, missing confidence interval.

The combined output produces a revision guide that transforms advocacy into rigorous analysis: cite multiple studies across different worker types, acknowledge countervailing evidence, state confidence intervals, and label opinion as opinion.