MCP Recipe to Find Top Revenue Channels.
Attribution models stress-tested with first principles, statistical methodology audited for false confidence , make budget decisions on truth, not dashboards
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your CMO presents a board deck: 'Our Google Ads campaign delivered 340% ROI last quarter. We should increase budget by 50%.' Phase 1: the agent runs `validate_deep_analysis`.
First Principles: what does '340% ROI' actually mean? The team used last-click attribution , crediting 100% of the conversion to the final touchpoint before purchase.
But last-click ignores the 6 touchpoints that preceded it: a blog post (organic search), a retargeting ad (display), an email nurture (3 emails), and a LinkedIn post (social).
The Google Ad was the last click, but was it the cause of the conversion , or just the final step in a journey that was already in motion? Inversion: what would happen if we cut Google Ads entirely? If 70% of those conversions would have happened anyway (through other channels), the true ROI is not 340% , it is closer to 102%.
The last-click model inflates Google Ads ROI by 3.3x. Hidden Assumptions: (1) last-click = causation (false , it is correlation at best), (2) all conversions are incremental (false , some buyers were already committed), (3) the model treats channels as independent (false , they interact).
Second-Order Consequence: if the CMO increases Google Ads budget by 50% based on inflated ROI, the incremental spend will deliver diminishing returns.
The first $100K of Google Ads captured high-intent buyers. The next $50K will target increasingly marginal audiences, dropping actual ROI while the dashboard continues to show inflated numbers.
Phase 2: the agent runs `validate_data_analysis`. Sample Size: 340 conversions attributed to Google Ads. For a 340% ROI claim, the margin of error at 95% confidence is 18%.
Actual ROI range: 280-400%. Correlation vs. Causation: last-click attribution is purely correlational. To establish causation, you need incrementality testing , a holdout group that does not see Google Ads.
Without this, the 340% figure is a correlation masquerading as causation. Selection Bias: Google Ads targets high-intent searchers , people already looking for your product category.
These buyers have the highest conversion rate regardless of the ad. Attributing their purchase to the ad conflates audience quality with ad effectiveness.
MCP Server Orchestration: 2 MCP Servers, one intelligent agent
Connect Deep Analyst Prover and Data Analysis Prover MCP servers so your AI agent audits marketing attribution models and ROI claims with both strategic rigor and statistical precision. Phase 1: the agent runs the Deep Analyst Prover to decompose the attribution model using first-principles thinking , what does 'attribution' actually measure? What assumptions are embedded? What happens when those assumptions break? Phase 2: the agent runs the Data Analysis Prover to audit the statistical methodology , sample sizes, confidence intervals, correlation vs. causation, and selection bias. The result is marketing budget decisions based on verified truth, not the comforting fiction that dashboards present.
Deep Analyst Prover
triggerDecomposes attribution models using first principles, applies inversion, and maps hidden assumptions
validate_deep_analysis Data Analysis Prover
actionAudits statistical methodology , sample sizes, confidence intervals, correlation vs. causation, selection bias
validate_data_analysis Run This Automation Today
Connect Claude, ChatGPT, Cursor, or any AI agent to the Vinkius catalog and run this automation in minutes.
Build Your Own MCP
Turn any internal API into an MCP server. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Connect & Automate
The 2 servers this recipe uses are ready in the catalog. Connect them once, paste a prompt, and your AI runs the full workflow.
- Deep Analyst Prover & Data Analysis Prover ready in the catalog right now
- Add more from 4,700+ servers whenever you need
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers and recipes added every week
Superpowers you didn't know your AI had
The Vinkius catalog gives your agent access to 4,700+ MCP servers and the intelligence to combine them. Imagine never logging into another dashboard. Your AI handles the work across every tool, in one conversation. That's what this infrastructure was built for.
Cross-Platform Intelligence
Your agent doesn't just connect to tools. It understands the relationships between them. Data flows where it needs to go, automatically, with full context preserved across every platform.
Contextual Reasoning
Every decision your agent makes considers the full picture. It reads CRM data, checks calendars, reviews conversation history, and acts on everything at once. Not step by step. All at once.
Productivity at Scale
What used to take 45 minutes across five different dashboards now takes one sentence. Your agent runs the entire workflow end to end while you focus on decisions that actually matter.
Zero-Config Reliability
No API keys to paste. No webhooks to configure. No YAML to debug. Connect your MCP servers once, and your agent handles the rest. Every time, without intervention.
Made for
exactly this
Your AI agent taps into the entire Vinkius MCP catalog to handle these for you. You describe what you need. It does the rest.
CMOs preparing board presentations who need attribution claims that survive scrutiny from financially sophisticated board members and investors
Performance marketing managers defending or requesting budget increases who need data-backed arguments that distinguish real ROI from dashboard theater
Marketing analysts evaluating channel mix and attribution models who need a systematic framework for identifying hidden assumptions and statistical weaknesses
CFOs and revenue leaders auditing marketing spend efficiency who need a rigorous analytical lens that goes beyond dashboard metrics to reveal true incrementality
Frequently Asked Questions About This MCP Server Orchestration
Which MCP servers do I need?
Two: Deep Analyst Prover and Data Analysis Prover.
Does this work with Claude Desktop, Cursor or Windsurf?
Yes. Any AI client that supports the Model Context Protocol works.
Does this replace our attribution platform?
No. It audits the conclusions your attribution platform produces. The platform provides data. This workflow questions whether the data means what you think it means.
What attribution models does this audit?
Any model , last-click, first-click, linear, time-decay, position-based, data-driven, and multi-touch. Each model embeds different assumptions, and the Deep Analyst Prover identifies the specific assumptions in your model.
Can this help with marketing mix modeling?
Yes. Marketing mix modeling has its own assumptions (linearity, stationarity, no interaction effects) that the Deep Analyst decomposes and the Data Analysis Prover audits for statistical validity.
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MCP servers used in this workflow
Deep Analyst Prover
The `validate_deep_analysis` tool forces your AI client to perform multi-model intellectual analysis on any complex problem. It goes way beyond surface-level answers by systematically decomposing questions, listing core assumptions, stacking multiple mental models (First Principles, Second-Order, Inversion), steelmanning the opposition, mapping three levels of consequences, and running a pre-mortem risk assessment. Stop getting generic summaries; start getting deep, actionable insight.
Data Analysis Prover
The Data Analysis Prover runs any statistical claim through five mandatory checks—sample quality, causal validity, distribution assumptions, effect size reporting, and chart honesty. It forces your AI client to act like a senior statistician reviewing a research paper, catching flaws that standard models miss.