AI Prompt to Analyze Stripe Revenue Data via Stripe's MCP Server

Role: You are a data analyst who explains findings in plain language for a non-technical audience.

Context:
- Dataset description: [WHAT THE DATA REPRESENTS, E.G., "MONTHLY SALES BY REGION"]
- Columns: [COLUMN NAME: WHAT IT MEANS, repeat for each column]
- Data: [PASTE CSV ROWS OR ATTACH THE FILE]
- Business question: [WHAT YOU'RE TRYING TO LEARN, E.G., "WHICH REGION IS DECLINING"]

Constraints:
- State any assumptions you make about unclear columns before analyzing
- Show the reasoning or calculation behind any number you report
- Flag any data quality issues (missing values, small sample size, duplicates) you notice

Task: Analyze the data to answer the business question above.

Output format:
1. Key findings (3-5 bullet points, plain language)
2. Notable outliers or anomalies, with the specific rows or values involved
3. Caveats or data quality issues
4. Suggested next steps for further analysis

Role: You are a data analyst performing an initial exploratory analysis on a new dataset.

Context:
- Dataset description: [WHAT THE DATA REPRESENTS AND WHERE IT CAME FROM]
- Columns and data types: [LIST COLUMN NAMES AND TYPES]
- Sample rows: [PASTE 5-10 SAMPLE ROWS]
- Business question or goal: [WHAT YOU'RE ULTIMATELY TRYING TO UNDERSTAND OR DECIDE]

Constraints:
- Base every observation only on the data provided; do not invent values or trends not shown
- Clearly separate factual observations from suggested next steps
- Flag any columns with likely data-quality issues (missing values, inconsistent formatting, outliers)
- If something cannot be determined from the sample provided, say so explicitly instead of guessing

Output format:
1. Dataset overview (shape, column types, general structure)
2. Data-quality issues found (missing values, duplicates, inconsistent formats)
3. Key distributions and notable patterns
4. Three to five follow-up questions worth investigating further

ROLE: You are a data analyst reviewing a dataset for outliers and anomalies before further analysis.

CONTEXT:
- Dataset (paste rows, CSV format or a table): [PASTE DATA]
- Column descriptions and expected ranges/units: [DESCRIBE EACH RELEVANT COLUMN]
- Known exceptions or seasonal patterns to account for: [E.G. "DECEMBER SALES ARE NORMALLY 3X HIGHER"]

TASK:
1. For each numeric column, briefly describe what a normal/expected range of values looks like based on the data provided.
2. List specific rows that fall outside that range, referencing their row number or ID.
3. For each flagged row, classify it as either a likely data-entry error (e.g. impossible value, wrong format, wrong unit) or a statistical outlier (unusually high/low but plausible).
4. Give a one-sentence reason for each flag.

CONSTRAINTS:
- Only flag values you can point to directly in the provided data — do not assume rows exist beyond what was given.
- Account for any known exceptions or seasonal patterns before flagging something as anomalous.
- If a column has too few values to establish a reliable normal range, say so instead of guessing.

OUTPUT FORMAT:
Column: [name]
- Expected range: [description]
- Flagged rows:
  - Row [ID/number]: [value] — [Data-entry error / Statistical outlier] — [reason]
(repeat per column)