mcp server
squirrelscan
Website QA for your coding agent: audit SEO, performance, security, accessibility over MCP.
Description as published by the maintainer. Source
- version 1.0.2
- active
- security
active — Most recent push to the repository was 2026-08-05. Dashed tags are derived by ZBS Index from the published description, not stated by the maintainer.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| GitHub stars | 251 | Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. | cumulative, all time | GitHub | |
| Last commit | 2026-08-05 | Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. | point in time | GitHub | |
| Open issues | 9 | Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. | as of fetch | GitHub | |
| Latest published version | 1.0.2 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-07-19 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| License | MIT | Licence GitHub detected in the repository. Detection can be wrong; the LICENSE file is authoritative. | as of fetch | GitHub | |
| First listed in the MCP Registry | 2026-07-19 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| repository status | active | The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. | as of fetch | GitHub |
Where to get it
Related, by what their authors tagged them
-
io.github.carloshpdoc/memorydetective
— last commit 2026-05-25, shares performance
iOS leak/perf debugging via MCP: memgraph cycles, .trace analysis, SourceKit-LSP bridging.
-
io.github.dragnoir/shopify-theme-inspector
— last commit 2026-08-05, shares performance
Profile Shopify Liquid performance, find slow theme code, and explain fixes in plain language.
-
WebAnatomy
— last commit 2026-07-02, shares website
Help your AI improve landing pages, grounded in 3,500+ scored sections and 500 real pages.
-
Crosby, TX Weather
— last commit 2026-08-05, shares website
Crosby, TX weather, air quality, tropics, floods, fishing, pollen, roads, radar, news & schools.
-
ShipStatic
— last commit 2026-08-06, shares website
Deploy websites from AI agents. Free at mcp.shipstatic.com. Install for the full toolset.
-
io.github.commitshow/audit
— last commit 2026-07-24, shares audit, cli
Score any public GitHub repo 0-100 against the commit.show audit rubric.
-
io.github.commitshow/legitshow-search
— last commit 2026-07-24, shares audit, cli
Search launched software (SaaS, AI tools, MCP servers) by measured production-readiness.
-
com.clauxel.codexrunledger/codexrunledger-mcp
— last commit 2026-05-19, shares audit
Codex run receipts your reviewer can trust.
-
com.clauxel.toolcallwitness/toolcallwitness-mcp
— last commit 2026-05-19, shares audit
A witness layer for AI agent tool calls.
-
io.github.Apex-Foundation/copilot-mcp
— last commit 2026-07-30, shares audit
Web3 founder diligence: code audit, jurisdiction, fund matching, portfolio, scoring.
These share tags the maintainers applied themselves, such as performance, website, audit, cli. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.
This is not a recommendation and not a test result. It is a map of what the authors said their work is about.
How the author describes it
Topics the maintainer set on GitHub: agent, audit, cli, llm, performance, seo, website.
Bring your own setup
We take apart real AI setups every week and show what broke, what cost too much, and what the trace actually said. If you run agents on real work, that is where the useful conversation is.
Join ZBS AI Practice Lab