Rigor MCP vs Figma Context MCP
A side-by-side look at the Rigor MCP and Figma Context 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 Figma Context MCP for architecture & design as a local process. 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 Figma Context MCP when
- You need dedicated capabilities in the Architecture & Design domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: API key required.
- You have the keys it needs:
FIGMA_API_KEY, PORT. - You want tools like
get_figma_data, download_figma_images.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Data Science Tools | Architecture & Design |
| Quality signal | 56/100 · Good | 71/100 · Great |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | API key required |
| Pricing | Not declared | Free |
| Required env vars | None listed | FIGMA_API_KEYPORT |
| Verified owner | Community listing | Community listing |
| GitHub stars | 1 | 16,061 |
| Directory activity | 1 views 0 installs 0 upvotes | 10 views 0 installs 0 upvotes |
| Tools | 37 | 2 |
| Full listing | View Rigor MCP | View Figma Context 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
Figma Context MCP tools 2
get_figma_dataGet comprehensive Figma file data including layout, content, visuals, and component informationdownload_figma_imagesDownload SVG and PNG images used in a Figma file based on the IDs of image or icon nodes. Images will be saved relative to the server's image directory: /home/user
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"
]
}
}
}
Figma Context MCP
{
"mcpServers": {
"glips-figma-context-mcp": {
"command": "npx",
"args": [
"-y",
"figma-developer-mcp"
],
"env": {
"FIGMA_API_KEY": "YOUR_FIGMA_API_KEY_HERE",
"PORT": "YOUR_PORT_HERE"
}
}
}
}
Frequently asked questions
Rigor MCP is categorized under Data Science Tools and uses a local stdio subprocess. Figma Context MCP belongs to Architecture & Design and uses a local stdio subprocess. Pick Rigor MCP for data science tools work and Figma Context MCP for architecture & design.
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.
Rigor MCP has no declared pricing — check the project's own site. Figma Context MCP is listed as free. Auth: api key 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 glips-figma-context-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. Figma Context MCP has 71/100 (Great) with 10 views, 0 installs and 16,061 GitHub stars.