Rigor MCP vs Markdownify MCP
A side-by-side look at the Rigor MCP and Markdownify MCP MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Rigor MCP for data science tools work as a local process; choose Markdownify MCP for data science tools. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Rigor MCP when
- You need dedicated capabilities in the Data Science Tools domain.
- You prefer a local stdio subprocess transport.
- You want tools like
one_sample_t_test, two_sample_t_test, paired_t_test.
Choose Markdownify MCP when
- You need dedicated capabilities in the Data Science Tools domain.
- Its auth model fits you: No auth required.
- You have the keys it needs:
MARKITDOWN_PATH, REPOMIX_PATH, MD_ALLOWED_PATHS, MD_SHARE_DIR. - You want tools like
youtube-to-markdown, pdf-to-markdown, bing-search-to-markdown.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Data Science Tools | Data Science Tools |
| Quality signal | 56/100 · Good | 56/100 · Good |
| Install command | Confirmed | Not confirmed yet |
| Transport | Local subprocess (stdio) | — |
| Auth | Not declared | No auth required |
| Pricing | Not declared | Free |
| Required env vars | None listed | MARKITDOWN_PATHREPOMIX_PATHMD_ALLOWED_PATHSMD_SHARE_DIR |
| Verified owner | Community listing | Community listing |
| GitHub stars | 1 | 2,996 |
| Directory activity | 1 views 0 installs 0 upvotes | 4 views 0 installs 0 upvotes |
| Tools | 37 | 10 |
| Full listing | View Rigor MCP | View Markdownify MCP |
Tools & capabilities
Rigor MCP tools 37
one_sample_t_testTest whether a sample's mean differs from a hypothesized value mu0. Returns the t-statistic, degrees of freedom, two-tailed p-value, a confidence interval for the mean, and any assumption warnings.two_sample_t_testTest whether two independent samples have different means. Defaults to Welch's t-test (does not assume equal variances); pass equal_var=true for the classic pooled-variance test.paired_t_testTest whether the mean difference between paired observations (e.g. before/after measurements on the same subjects, or matched pairs) is zero. a[i] and b[i] must be the two measurements of the same pair -- use two_sample_t_test instead if the two samples are independent (different subjects in each group). Returns the t-statistic, degrees of freedom (n-1), two-tailed p-value, a confidence interval for the mean difference, a citation, and assumption warnings.one_proportion_z_testTest whether an observed proportion (successes out of n) differs from a hypothesized proportion p0 -- e.g. "is this coin fair (p0=0.5) given 55 heads in 100 flips?" Uses the normal approximation, which degrades for small n or p0 near 0 or 1; a warning is included when that assumption looks shaky. Returns the z-statistic, two-tailed p-value, a confidence interval for the true proportion, a citation, and warnings.two_proportion_z_testTest whether two independent proportions differ -- the standard test behind comparing conversion rates between two groups (e.g. an A/B test). Returns the z-statistic, two-tailed p-value, a confidence interval for the difference in proportions, a citation, and warnings.chi_square_goodness_of_fitTest whether observed category counts match an expected distribution -- e.g. "are these six days-of-week signup counts evenly distributed, or skewed towards weekends?" Returns the chi-squared statistic, degrees of freedom (len-1), p-value, a citation, and a warning if any expected count is below 5 (the usual threshold below which this approximation gets unreliable).chi_square_independenceTest whether the row and column variables of a contingency table are independent (e.g. "does group membership relate to outcome?"). Returns the chi-squared statistic, degrees of freedom, p-value, a citation, and a warning if any expected cell count is below 5 (consider cramers_v afterwards for effect size).one_way_anovaTest whether three or more independent groups have different means -- e.g. comparing average order value across three marketing channels. A significant result means at least one group differs from the others, not which one -- follow up with pairwise two_sample_t_test calls (correcting for multiple comparisons via bonferroni_correction or benjamini_hochberg_correction) to find which. Returns the F-statistic, between/within degrees of freedom, p-value, a citation, and a warning if within-group df is small.levene_testTest whether two or more groups have equal population variances (homogeneity of variance) -- use this to decide equal_var for two_sample_t_test, or to sanity-check one_way_anova's equal-variance assumption. Uses the Brown-Forsythe variant (deviations from each group's median), more robust to non-normal data than the original mean-based Levene's test. Returns the same shape as one_way_anova (it's computed as one internally, on absolute deviations from each group's median).fisher_exact_testTest whether the row and column variables of a 2x2 contingency table are independent -- exact (via the hypergeometric distribution over all tables with the same margins), unlike chi_square_independence's chi-squared approximation. Use this instead whenever chi_square_independence warns an expected cell count is below 5, or whenever the sample is small. 2x2 tables only. Returns the sample odds ratio as ``statistic`` (can be inf/0 for a zero cell), a two-tailed p-value, a citation, and warnings.mcnemar_testTest whether two paired proportions are equal -- e.g. the same subjects' yes/no answers before and after an intervention, or two raters' calls on the same items. Use this instead of two_proportion_z_test whenever the "two groups" are actually the same subjects measured twice; two_proportion_z_test assumes independent groups and gets the standard error wrong for paired data. Yates continuity-corrected chi-squared, 1 df -- use mcnemar_exact_test instead when there are few discordant pairs (this warns when there are). Returns that chi-squared statistic, a p-value, and a confidence interval for the difference in marginal proportions.mcnemar_exact_testExact version of mcnemar_test: an exact binomial test (p=0.5) on the discordant pairs instead of the chi-squared approximation -- the small-sample-safe alternative mcnemar_test's own warning points to, the same relationship fisher_exact_test has to chi_square_independence. ``statistic`` is b-c (the raw discordant-pair imbalance); the p-value is two-tailed.- +25 more on the listing page
Markdownify MCP tools 10
youtube-to-markdownConvert YouTube videos to Markdownpdf-to-markdownConvert PDF files to Markdownbing-search-to-markdownConvert Bing search results to Markdownwebpage-to-markdownConvert web pages to Markdownimage-to-markdownConvert images to Markdown with metadataaudio-to-markdownConvert audio files to Markdown with transcriptiondocx-to-markdownConvert DOCX files to Markdownxlsx-to-markdownConvert XLSX files to Markdownpptx-to-markdownConvert PPTX files to Markdownget-markdown-fileRetrieve an existing Markdown file. File extension must end with: *.md, *.markdown.
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Rigor MCP
{
"mcpServers": {
"rigor-mcp": {
"command": "uvx",
"args": [
"rigor-mcp"
]
}
}
}
Markdownify MCP
No confirmed install command for Markdownify MCP yet — follow the setup steps linked from its listing page rather than a guessed package name.
Frequently asked questions
Rigor MCP is categorized under Data Science Tools and uses a local stdio subprocess. Markdownify MCP belongs to Data Science Tools and uses an install path we have not confirmed yet. Pick Rigor MCP for data science tools work and Markdownify MCP for data science tools.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client. Markdownify MCP has no confirmed install command yet — follow its own setup instructions.
Rigor MCP has no declared pricing — check the project's own site. Markdownify MCP is listed as free. Auth: no auth required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add rigor-mcp and zcaceres-markdownify-mcp as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Rigor MCP has a quality signal of 56/100 (Good) with 1 views, 0 installs and 1 GitHub stars. Markdownify MCP has 56/100 (Good) with 4 views, 0 installs and 2,996 GitHub stars.