io.github.barangaroo/crosstabs-mcp
PYPI · CROSSTABS-MCP · SCANNED AUG 19
Statistical crosstab analysis: chi-square, Fisher's exact, odds ratio, effect sizes
Available components
Dead
Every channel of this server has been unreachable for long enough to be marked dead. Its trust score is pinned to 0 and does not recover automatically: even once the server responds again it stays dead until it is manually reinstated.
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security50
- No malware found by supply-chain analysis.Pass
- Known CVEs could not be checked: we could not find this package in its registry.Unverified
- Install-script risk not yet assessed.Unverified
- Dependency health could not be checked: we could not find this package in its registry.Unverified
Provenance & Transparency0
- Repository check failed: the declared repository URL returned HTTP 404. See how to fix → View diagnostics → Fail
- Provenance check failed: the registry has no such package, so there's no artefact to verify. View diagnostics → Fail
- License check failed: the registry has no such package, so no license can be read.Fail
- Maintenance check failed: the registry has no such package, so there's no release history to read.Fail
- Security-disclosure policy not yet verified: we couldn't inspect the source repository.Unverified
Schema Quality & AI Usability69
- AI-judged instruction clarity (good).Pass
- Tool/resource definitions use about 2672 tokens (~68/item across 39 items; 39 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management47
- Stability observed for 14 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage67
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 0% of tool parameters carry a description.Fail
Tool Safety–
Not scored yet: our latest scan of this package recorded no findings here. That is usually because the category is newer than the scan, or because we have not captured the MCP schema this package serves yet. It fills in the next time we score it.
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
How do I install the io.github.barangaroo/crosstabs-mcp server?
io.github.barangaroo/crosstabs-mcp runs locally as a PyPI package, launched with uvx crosstabs-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · crosstabs-mcp
claude mcp add barangaroo-crosstabs-mcp -- uvx crosstabs-mcp
{
"mcpServers": {
"barangaroo-crosstabs-mcp": {
"command": "uvx",
"args": [
"crosstabs-mcp"
]
}
}
} {
"servers": {
"barangaroo-crosstabs-mcp": {
"command": "uvx",
"args": [
"crosstabs-mcp"
]
}
}
} codex mcp add barangaroo-crosstabs-mcp -- uvx crosstabs-mcp
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"barangaroo-crosstabs-mcp": {
"type": "local",
"command": [
"uvx",
"crosstabs-mcp"
],
"enabled": true
}
}
} openclaw mcp add barangaroo-crosstabs-mcp --command uvx --arg crosstabs-mcp
mcp_servers:
barangaroo-crosstabs-mcp:
command: "uvx"
args: ["crosstabs-mcp"] {
"McpServers": {
"barangaroo-crosstabs-mcp": {
"Transport": "stdio",
"Command": "uvx",
"Arguments": [
"crosstabs-mcp"
]
}
}
} assistant mcp add barangaroo-crosstabs-mcp -t stdio -c uvx -a crosstabs-mcp
{
"mcpServers": {
"barangaroo-crosstabs-mcp": {
"command": "uvx",
"args": [
"crosstabs-mcp"
]
}
}
} Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 19 Aug 26 −11
The score moved with no change recorded against any check. We would rather say so than guess.
- 18 Aug 26 −11
The score moved with no change recorded against any check. We would rather say so than guess.
- 17 Aug 26 −10
The score moved with no change recorded against any check. We would rather say so than guess.
- 16 Aug 26 −11
The score moved with no change recorded against any check. We would rather say so than guess.
- 15 Aug 26 −11
The score moved with no change recorded against any check. We would rather say so than guess.
- 14 Aug 26 0
- Install scripts: pass → unverified ▼ security
- The scripts that run when this package is installed changed: build_backend security
- 13 Aug 26 0
- Known CVEs: partial → unverified ▼ security
- Dependency health: partial → unverified ▼ functional
- Stability: unverified → 0.27 ▲ functional
- 12 Aug 26 −3
- Known CVEs: pass → partial ▼ security
- Dependency health: 0.98 → partial functional
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 19 Aug 2026 · Analysed pypi/crosstabs-mcp@1.0.0
Provenance Package not found
The registry answered that no such package exists, so no provenance could be looked up.
| Result | Package not found |
|---|---|
| Ecosystem | pypi |
Background: How many MCP packages publish verified provenance →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
attributable_risk ~65
Attributable risk measures for epidemiological studies. Args: matrix: 2×2 contingency table (exposed/unexposed × outcome) Returns: AR, RRR, PAR, PAF, and other risk measures
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
bonferroni_correction ~74
Bonferroni correction for multiple testing. Args: p_values: List of p-values to correct alpha: Family-wise error rate (default 0.05) Returns: Adjusted p-values and significance
| Name | Type | Req | Description |
|---|---|---|---|
| alpha | number | – | – |
| p_values | array | yes | – |
No output schema declared.
No examples provided.
breslow_day_test ~68
Breslow-Day test for homogeneity of odds ratios across strata. Args: tables: List of 2×2 contingency tables (one per stratum) Returns: Test statistic, p-value, and whether ORs are homogeneous
| Name | Type | Req | Description |
|---|---|---|---|
| tables | array | yes | – |
No output schema declared.
No examples provided.
check_assumptions ~52
Check chi-square test assumptions. Verifies expected cell frequencies and provides recommendations. Args: matrix: Contingency table Returns: Assumption validity and recommendations
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
chi_square_test ~62
Perform Pearson's chi-square test of independence. Args: matrix: 2D contingency table as list of lists Returns: Test statistic, p-value, effect size (Cramér's V), and interpretation
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
chi_square_yates ~58
Chi-square with Yates continuity correction for 2×2 tables. Args: matrix: 2×2 contingency table Returns: Yates-corrected chi-square and p-value
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
cmh_test ~80
Cochran-Mantel-Haenszel test for stratified 2×2 tables. Tests association while controlling for a stratifying variable. Args: tables: List of 2×2 contingency tables (one per stratum) Returns: CMH chi-square, common odds ratio, and interpretation
| Name | Type | Req | Description |
|---|---|---|---|
| tables | array | yes | – |
No output schema declared.
No examples provided.
cohens_kappa ~52
Cohen's Kappa for inter-rater agreement (unweighted). Args: matrix: Square confusion matrix Returns: Kappa coefficient with CI and interpretation
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
correspondence_analysis ~81
Correspondence analysis for visualizing associations. Args: matrix: Contingency table row_labels: Optional row labels col_labels: Optional column labels Returns: 2D coordinates for biplot and explained variance
| Name | Type | Req | Description |
|---|---|---|---|
| col_labels | array | – | – |
| matrix | array | yes | – |
| row_labels | array | – | – |
No output schema declared.
No examples provided.
cramers_v ~60
Calculate Cramér's V effect size (standard and bias-corrected). Args: matrix: Contingency table Returns: Standard and bias-corrected Cramér's V with interpretation
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
crosstab_from_csv ~87
Parse CSV and create contingency table from two columns. Args: csv_text: CSV content as string row_var: Name of row variable column col_var: Name of column variable column Returns: Contingency table with labels
| Name | Type | Req | Description |
|---|---|---|---|
| col_var | string | yes | – |
| csv_text | string | yes | – |
| row_var | string | yes | – |
No output schema declared.
No examples provided.
crosstab_from_data ~85
Create contingency table from raw data. Args: data: List of data objects with row and column variables row_var: Name of row variable col_var: Name of column variable Returns: Contingency table with labels
| Name | Type | Req | Description |
|---|---|---|---|
| col_var | string | yes | – |
| data | array | yes | – |
| row_var | string | yes | – |
No output schema declared.
No examples provided.
detect_outliers ~84
Detect outliers in numeric data. Args: values: List of numeric values method: "zscore" or "iqr" threshold: Z-score threshold (default 2.0) Returns: Outlier statistics and identified outliers
| Name | Type | Req | Description |
|---|---|---|---|
| method | string | – | – |
| threshold | number | – | – |
| values | array | yes | – |
No output schema declared.
No examples provided.
effect_size ~52
Calculate multiple effect sizes for contingency table. Args: matrix: Contingency table Returns: Cramér's V (corrected), phi, and contingency coefficient
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
fdr_correction ~74
Benjamini-Hochberg false discovery rate correction. Args: p_values: List of p-values to correct alpha: FDR level (default 0.05) Returns: Adjusted significance with FDR control
| Name | Type | Req | Description |
|---|---|---|---|
| alpha | number | – | – |
| p_values | array | yes | – |
No output schema declared.
No examples provided.
fishers_exact ~57
Fisher's exact test for 2×2 tables. Args: matrix: 2×2 contingency table Returns: Odds ratio, exact p-value, and 95% CI
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
g_test ~59
G-test (likelihood ratio test) for contingency tables. More accurate than chi-square for small samples. Args: matrix: Contingency table Returns: G statistic, p-value, effect size
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
goodman_kruskal_gamma ~55
Goodman-Kruskal's gamma for ordinal association. Args: matrix: Contingency table with ordinal rows and columns Returns: Gamma coefficient with CI
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
kendalls_tau ~64
Kendall's tau-b rank correlation. Args: ranks_x: First set of ranks ranks_y: Second set of ranks Returns: Tau coefficient and p-value
| Name | Type | Req | Description |
|---|---|---|---|
| ranks_x | array | yes | – |
| ranks_y | array | yes | – |
No output schema declared.
No examples provided.
lambda_coefficient ~52
Goodman-Kruskal Lambda (proportional reduction in error). Args: matrix: Contingency table Returns: Lambda values (asymmetric and symmetric)
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
linear_trend_test ~104
Linear-by-linear association test (Mantel-Haenszel trend test). Args: matrix: Contingency table row_scores: Optional numeric scores for rows (default: 0, 1, 2, ...) col_scores: Optional numeric scores for columns Returns: Z-statistic and p-value for linear trend
| Name | Type | Req | Description |
|---|---|---|---|
| col_scores | array | – | – |
| matrix | array | yes | – |
| row_scores | array | – | – |
No output schema declared.
No examples provided.
mcnemar_test ~57
McNemar's test for paired categorical data. Args: matrix: 2×2 table of paired outcomes Returns: Test statistic and both asymptotic and exact p-values
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
monte_carlo_chi_square ~85
Monte Carlo simulation for exact chi-square p-value. Args: matrix: Contingency table n_sim: Number of simulations (default 10000) seed: Random seed for reproducibility Returns: Simulated p-value
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
| n_sim | integer | – | – |
| seed | integer | – | – |
No output schema declared.
No examples provided.
mosaic_plot_data ~78
Generate coordinates for mosaic plot visualization. Args: matrix: Contingency table row_labels: Optional labels for rows col_labels: Optional labels for columns Returns: Cell coordinates with residual-based coloring
| Name | Type | Req | Description |
|---|---|---|---|
| col_labels | array | – | – |
| matrix | array | yes | – |
| row_labels | array | – | – |
No output schema declared.
No examples provided.
odds_ratio ~59
Calculate odds ratio with 95% confidence interval for 2x2 table. Args: matrix: 2×2 contingency table [[a,b],[c,d]] Returns: Odds ratio and confidence interval
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
phi_coefficient ~47
Calculate phi coefficient for 2×2 table. Args: matrix: 2×2 contingency table Returns: Phi coefficient and interpretation
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
post_hoc_chi_square ~66
Full post-hoc analysis after chi-square test. Includes standardized residuals, adjusted residuals, and chi-square contributions by cell. Args: matrix: Contingency table Returns: Complete post-hoc analysis
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
power_analysis ~134
Power or sample size calculation for comparing two proportions. Args: p1: Proportion in group 1 p2: Proportion in group 2 n: Sample size per group (if provided, calculates power) alpha: Significance level (default 0.05) power: Target power if calculating sample size (default 0.80) Returns: Either calculated power or required sample size
| Name | Type | Req | Description |
|---|---|---|---|
| alpha | number | – | – |
| n | number | – | – |
| p1 | number | yes | – |
| p2 | number | yes | – |
| power | number | – | – |
No output schema declared.
No examples provided.
proportion_ci ~83
Calculate confidence interval for a proportion. Args: successes: Number of successes total: Total sample size method: "wilson" (recommended), "agresti", or "normal" Returns: Proportion with 95% CI
| Name | Type | Req | Description |
|---|---|---|---|
| method | string | – | – |
| successes | number | yes | – |
| total | number | yes | – |
No output schema declared.
No examples provided.
recommend_test ~64
Recommend appropriate statistical tests for your data. Args: matrix: Contingency table data_type: "categorical" or "ordinal" Returns: Ranked test recommendations with rationales
| Name | Type | Req | Description |
|---|---|---|---|
| data_type | string | – | – |
| matrix | array | yes | – |
No output schema declared.
No examples provided.
relative_risk ~57
Calculate relative risk with 95% confidence interval for 2x2 table. Args: matrix: 2×2 contingency table Returns: Relative risk, CI, and individual risks
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
risk_difference ~65
Calculate risk difference (absolute risk reduction) for 2x2 table. Also calculates NNT (Number Needed to Treat). Args: matrix: 2×2 contingency table Returns: Risk difference, CI, and NNT
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
somers_d ~59
Somers' D (asymmetric ordinal measure). Args: matrix: Contingency table with ordinal rows and columns Returns: d(Y|X), d(X|Y), and symmetric versions
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
spearmans_rho ~65
Spearman's rank correlation coefficient. Args: ranks_x: First set of ranks ranks_y: Second set of ranks Returns: Rho coefficient and p-value
| Name | Type | Req | Description |
|---|---|---|---|
| ranks_x | array | yes | – |
| ranks_y | array | yes | – |
No output schema declared.
No examples provided.
stacked_bar_data ~105
Generate data for stacked bar chart. Args: matrix: Contingency table by_rows: If True, groups are rows; if False, groups are columns row_labels: Optional labels for rows col_labels: Optional labels for columns Returns: Stacked bar chart data with proportions
| Name | Type | Req | Description |
|---|---|---|---|
| by_rows | boolean | – | – |
| col_labels | array | – | – |
| matrix | array | yes | – |
| row_labels | array | – | – |
No output schema declared.
No examples provided.
standardized_residuals ~60
Calculate standardized residuals from contingency table. Values > |2| indicate cells that significantly deviate from expected. Args: matrix: Contingency table Returns: Residuals matrix with interpretation
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
tau_c ~49
Stuart's tau-c for rectangular ordinal tables. Args: matrix: Contingency table Returns: Tau-c coefficient with standard error and p-value
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
uncertainty_coefficient ~47
Uncertainty Coefficient (entropy-based association measure). Args: matrix: Contingency table Returns: Uncertainty coefficients and mutual information
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
No output schema declared.
No examples provided.
weighted_kappa ~67
Weighted Cohen's Kappa for ordinal agreement. Args: matrix: Square confusion matrix weights: "linear" or "quadratic" (default) Returns: Weighted kappa with CI and interpretation
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | yes | – |
| weights | string | – | – |
No output schema declared.
No examples provided.
What is the io.github.barangaroo/crosstabs-mcp server?
io.github.barangaroo/crosstabs-mcp is listed in the public MCP registry as io.github.barangaroo/crosstabs-mcp. Statistical crosstab analysis: chi-square, Fisher's exact, odds ratio, effect sizes. This page covers its PyPI package (crosstabs-mcp).
Is the io.github.barangaroo/crosstabs-mcp server safe to use?
io.github.barangaroo/crosstabs-mcp scores 0 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the io.github.barangaroo/crosstabs-mcp server expose?
io.github.barangaroo/crosstabs-mcp exposes 39 tools: chi_square_test, fishers_exact, mcnemar_test, g_test, odds_ratio, and 34 more. Their descriptions and schemas cost roughly 2,672 tokens of context every time the server is loaded.
Is the io.github.barangaroo/crosstabs-mcp server still maintained?
io.github.barangaroo/crosstabs-mcp has been unreachable for long enough that we treat it as dead, and its score is pinned to zero. We last reached this channel on 12 August 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.