Talk2Data InsightGenius

Name: Talk2Data InsightGenius
Author: quack2025

by quack2025

Overview Schema Related Servers Score Discussions

Python

Remote

SPSS InsightGenius API

Professional SPSS processing API + MCP server for market research. Upload .sav files, get crosstabs with significance testing, auto-detected question types, and publication-ready Excel exports. Includes AI-powered zero-config analysis.

Live: spss.insightgenius.io | API Docs: spss.insightgenius.io/docs | MCP: spss.insightgenius.io/mcp/sse

Quick Start

Option 1: Web UI (no code needed)

Open spss.insightgenius.io
Drag & drop your .sav file
Click Auto-Analyze for instant results, or configure manually:
- Select banner variables (demographics for columns)
- Choose stubs (questions for rows)
- Enable Top 2 Box / Means
Click Generate Excel → download your tabulation

Option 2: Auto-Analyze (zero config)

curl -X POST https://spss.insightgenius.io/v1/auto-analyze \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F "file=@survey.sav" \
  -o auto_analysis.xlsx

AI auto-detects banners, groups variables into MRS/Grid, applies nets, and generates a complete Excel.

Option 3: Full Control

curl -X POST https://spss.insightgenius.io/v1/tabulate \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F "file=@survey.sav" \
  -F 'spec={
    "banners": ["gender", "region", "age_group"],
    "stubs": ["_all_"],
    "significance_level": 0.95,
    "include_means": true,
    "nets": {"sat_overall": {"Top 2 Box": [4,5], "Bottom 2 Box": [1,2]}},
    "mrs_groups": {"Brand_Awareness": ["AWARE_A","AWARE_B","AWARE_C"]}
  }' -o tabulation.xlsx

Option 4: Python

import requests, json

resp = requests.post(
    "https://spss.insightgenius.io/v1/tabulate",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    files={"file": open("survey.sav", "rb")},
    data={"spec": json.dumps({
        "banners": ["gender", "region"],
        "stubs": ["_all_"],
        "include_means": True,
        "significance_level": 0.95,
    })}
)
with open("tabulation.xlsx", "wb") as f:
    f.write(resp.content)
print(f"Done: {resp.headers['X-Stubs-Success']} tables generated")

Option 5: MCP (for AI agents)

Connect to https://spss.insightgenius.io/mcp/sse and use any of the 12 tools. Files are passed as base64.

Endpoints (14)

Method	Endpoint	Description
POST	`/v1/auto-analyze`	Zero-config — upload .sav, get complete Excel (AI-detected banners, MRS, grids, nets)
POST	`/v1/tabulate`	Full tabulation → Excel with sig letters, nets, means, MRS, grids, custom groups. Accepts optional .docx Reporting Ticket.
POST	`/v1/metadata`	Variable metadata + suggested banners + detected groups + preset nets
POST	`/v1/frequency`	Frequency table (counts, %, mean, std, median)
POST	`/v1/crosstab`	Single crosstab with sig letters (A/B/C) + chi-square p-value
POST	`/v1/correlation`	Correlation matrix (Pearson/Spearman/Kendall) with p-values
POST	`/v1/anova`	One-way ANOVA with Tukey HSD post-hoc comparisons
POST	`/v1/gap-analysis`	Importance-Performance gap analysis with quadrants
POST	`/v1/satisfaction-summary`	Compact T2B/B2B/Mean for multiple scale variables
POST	`/v1/process`	Multi-operation pipeline (auto-detect or manual)
POST	`/v1/convert`	Convert .sav → xlsx, csv, parquet, dta
POST	`/v1/parse-ticket`	Parse Reporting Ticket .docx → tab plan (Haiku AI)
GET	`/v1/health`	Health check + engine status
GET	`/v1/usage`	Usage stats for your API key

MCP Tools (12)

Tool	Description
`get_spss_metadata`	Variable metadata + auto-detect
`get_variable_info`	Single variable detail
`analyze_frequencies`	Frequency table
`analyze_crosstabs`	Crosstab with sig letters
`analyze_correlation`	Correlation matrix
`analyze_anova`	ANOVA + Tukey HSD
`analyze_gap`	Gap analysis with quadrants
`summarize_satisfaction`	Satisfaction summary
`create_tabulation`	Full tabulation → Excel (base64)
`auto_analyze`	Zero-config → Excel (base64)
`export_data`	Format conversion
`list_files`	Available tools

Tabulate Spec

Field	Type	Default	Description
`banners`	string[]	required	Demographics for columns (e.g., `["gender", "region"]`)
`stubs`	string[]	`["_all_"]`	Questions for rows (`_all_` = auto-select all)
`significance_level`	float	`0.95`	0.90, 0.95, or 0.99
`weight`	string	`null`	Weight variable name
`include_means`	bool	`false`	Add Mean row with T-test sig letters
`include_total_column`	bool	`true`	Total as first column
`output_mode`	string	`"multi_sheet"`	`"multi_sheet"` or `"single_sheet"`
`nets`	object	`null`	Per-variable net definitions
`mrs_groups`	object	`null`	MRS groups: `{"name": ["var1", "var2"]}`
`grid_groups`	object	`null`	Grid groups: `{"name": {"variables": [...], "show": ["t2b","mean"]}}`
`custom_groups`	array	`null`	Custom breaks with AND conditions
`title`	string	`""`	Report title

Excel Output

Summary sheet: file info, column legend (A=London, B=South East...), stub index
One sheet per stub: headers → letters → base (N) → data with pct% SIG_LETTERS → nets → means
MRS sheets: one per group, percentages can exceed 100%
Grid sheets: compact T2B/B2B/Mean summary
Significance letters in red, nets in green rows
Freeze panes for scrolling

Significance Testing

Column proportion z-test with letter notation (A/B/C):

Each banner category gets a letter (e.g., Male=A, Female=B, London=C, North=D)
Each cell tested vs every other column
Significantly higher → other column's letter appears (e.g., 68.6% E means sig higher than column E)
Supports weighted (Kish effective-n) and unweighted
Means tested with independent T-test
Confidence levels: 90%, 95%, 99%

Authentication

All endpoints require: Authorization: Bearer sk_live_... or sk_test_...

Rate Limits

Plan	Requests/min	Max file	Price
Free	10	5 MB	$0
Growth	60	50 MB	$29/mo
Business	200	200 MB	$99/mo
Enterprise	Unlimited	500 MB	Custom

Error Codes

Code	HTTP	Meaning
`UNAUTHORIZED`	401	Missing/invalid API key
`FORBIDDEN`	403	Valid key, wrong scope
`RATE_LIMIT_EXCEEDED`	429	Too many requests
`INVALID_FILE_FORMAT`	400	Not a .sav file
`FILE_TOO_LARGE`	413	Exceeds plan limit
`VARIABLE_NOT_FOUND`	400	Variable doesn't exist
`PROCESSING_FAILED`	500	Engine error
`PROCESSING_TIMEOUT`	504	Exceeded time limit

Local Development

git clone https://github.com/quack2025/spss-insightgenius-api.git
cd spss-insightgenius-api
pip install -r requirements.txt
cp .env.example .env  # Edit with your API key hash
python main.py        # → http://localhost:8000
python -m pytest tests/ -v  # 68 tests

Stack

Layer	Technology
API	FastAPI + Pydantic v2
Engine	QuantipyMRX (crosstab, sig testing, auto-detect, MRS, NPS)
AI	Claude Haiku (ticket parsing, smart labels, executive summary)
Auth	API keys (SHA256, no DB)
Rate Limiting	Redis (fallback: in-memory)
MCP	FastMCP with SSE transport
Deploy	Railway (Docker, Gunicorn, 4 replicas, auto-deploy)

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