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Run descriptive statistics, hypothesis tests, regression, and broader statistical analysis via FastMCP.
This MCP tool appears to provide statistical analysis features and does not declare any required secrets or remote endpoints. Based on the available facts, the main concerns are the inherent local code-execution capability of such tools and supply-chain uncertainty due to low community adoption and unknown maintenance status.
The materials explicitly state that no keys or environment variables are required. No token request, credential storage, or third-party account authorization is described, so credential exposure risk appears low.
The materials state there are no remote endpoints, and the README does not describe any online service or data upload behavior. Based on known facts, there is no evidence of user data being sent to external hosts.
The system checks indicate that the tool has code-execution capability. For an MCP tool, this commonly means running local processes or executing related logic. The materials do not show unusually elevated privileges beyond its stated statistical purpose, but it should still be treated cautiously as a local execution tool.
The description indicates it performs statistical analysis, which typically requires handling user-provided data. However, the materials do not specify exact file paths, data scope, or resource boundaries it can read or write. No explicit overreach red flag is shown, but the data-access scope is insufficiently documented and should be constrained by least privilege.
A positive sign is that there is an open-source repository, which provides some auditability. However, it comes from a third-party registry, the license is undeclared, community adoption is 0 stars, maintenance status is unknown, and the README is missing, so overall maturity and audit confidence are limited, creating some supply-chain uncertainty.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "JeffersonStats" yet — see the docs or source repo.
Analyze conversion-rate data for Group A and Group B: provide descriptive statistics first, then run an independent-samples t-test, report significance level, p-value, and effect size, and explain the conclusion in plain language.
An analysis report with descriptive statistics, t-test results, significance judgment, and a clear plain-language conclusion.
I have data for sales, ad spend, discount rate, and visitor count. Build a regression model to determine which factors most affect sales, and output coefficients, significance, model fit, and any potential multicollinearity issues.
A regression analysis showing key drivers, statistical significance, model performance, and risk notes.
Create a comprehensive descriptive statistics report for this survey dataset, including mean, median, standard deviation, quantiles, outlier flags, and a summary of distribution characteristics suitable for a weekly report.
A business-ready descriptive statistics summary with key metrics and interpretation of data distributions.
Quickly compute data metrics and descriptive statistics with clear analytical outputs.
Run statistical analysis, probability calculations, and data processing through natural language.
Load datasets, compute statistics, and create charts for data exploration.
Analyze fantasy football stats, dynasty value, projections, and prospect data.
Explore CSV datasets with summaries, cleaning, correlations, and statistical tests.
Access and analyze official European statistics from the Eurostat API.