Same data. Same answer.
Every time.

Every MCP Analytics report includes the exact R script that generated it. Run it twice, get the same result. Run it next year, get the same result. Cite it in a paper, defend it in a meeting, audit it in a compliance review.

Run a free analysis → See a sample report

Why this matters

AI tools that generate analysis code on the fly produce different code — and different results — on every run. That makes the answer impossible to cite, audit, or trust for any decision that has consequences.

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Deterministic

The R modules are reviewed code, not generated code. Same inputs produce identical outputs — including the random seed used for any sampling, splits, or simulations. No drift, no surprises.

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The code is in your report

Every report includes a "Show the Code" appendix with the actual R script. Not a high-level summary — the exact script that produced the numbers above it. Copy it, run it locally, verify it.

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Citable methodology

Every report has a one-click citation in APA, MLA, Chicago, IEEE, and BibTeX. Link the live report URL or attach the PDF — both reference the same methodology, the same data, the same result.

Why "AI does the analysis" isn't enough

When an AI assistant writes code on every prompt, you get a different program every time — and often a different answer. That's fine for exploration. It's a problem for anything someone else has to trust.

LLM code generation Inconsistent

✗ New Python script written each run — same question can produce different code, different methods, different numbers.
✗ No version control. The "analysis" only exists in the chat session that produced it.
✗ Hallucinated outputs documented in independent reviews. Plausible-looking statistics that don't match the data.
✗ Cannot be cited — the source isn't a stable reference, it's a one-time conversation.
✗ No audit trail when a stakeholder asks "how did you get this number?"

MCP Analytics modules Reproducible

✓ Each module is a reviewed R script. Same data + same parameters always produces the same result.
✓ The R source code ships in every report. Anyone can read it, copy it, run it.
✓ Reports are persistent and searchable. Re-open any analysis from the library, any time.
✓ Citable in APA/MLA/Chicago/BibTeX. Defensible in a paper, a board deck, or a regulator review.
✓ AI handles interpretation and discovery. The numbers come from R. Best of both.

This is what's in your report

Below is an excerpt from a real telecom churn analysis. The same code runs every time, with the same edge-case handling, the same model specification, the same metrics. You don't have to take our word for it — the source ships in the report appendix.

#' ## Core Analysis Pipeline
#' All statistical computations happen once in `compute_shared()` and are
#' then distributed to individual report cards.

compute_shared <- function(df, params) {
  #' ### Step 1: Parameter Setup and Data Cleaning
  #' Two parameters govern the analysis: `confidence_level` controls prediction
  #' interval width, and `top_n_features` limits how many predictors appear in
  #' the ranking chart.
  confidence_level <- params$confidence_level %||% 0.95
  top_n <- as.integer(params$top_n_features %||% 10L)

  # Coerce total_charges — may arrive as character with blank strings
  df$total_charges <- suppressWarnings(as.numeric(df$total_charges))

  # Drop rows missing the two most critical fields
  df <- df[!is.na(df$monthly_charges) & !is.na(df$tenure), ]

  # Binary churn outcome: 1 = churned, 0 = retained
  df$churn_binary <- as.integer(trimws(as.character(df$churn)) == "Yes")

  #' ### Step 3: Logistic Regression Model
  #' We fit a binary logistic regression with 15 predictors covering contract,
  #' billing, service add-ons, and demographics. The model is wrapped in
  #' `tryCatch()` to handle edge cases gracefully.
  model <- tryCatch(
    glm(churn ~ ., data = model_df, family = binomial(link = "logit")),
    error = function(e) { message("Logistic regression failed: ", e$message); NULL }
  )
  ...
}

Excerpt from analytics__telecom__churn__customer_retention — the actual R source that runs in production. Full file: 584 lines, included in every report.

Citable, defensible, persistent

A report that exists only in a chat session can't be cited. Our reports are stable URLs and PDF documents with structured methodology blocks — built for the moment when someone asks "where did this number come from?"

APA Citation (auto-generated in every report) MCP Analytics. (2026). Telecom Customer Churn & Retention Analysis [Statistical analysis report]. Retrieved from https://mcpanalytics.ai/reports/...

BibTeX (also one-click) @misc{mcpanalytics2026churn,
  title = {Telecom Customer Churn & Retention Analysis},
  author = {{MCP Analytics}},
  year = {2026},
  url = {https://mcpanalytics.ai/reports/...}
}

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Same data. Same answer.Every time.