ClaudeR - RStudio MCP Server vs Google Analytics MCP
Two MCP servers from our data & analytics ranking, compared on live data - updated nightly, never sponsored.
Bottom line · 2026-09-18
monthly package install counts are comparable (11,271 vs 12,229); both are actively maintained, with commits inside the last month.
How they differ in kind
ClaudeR - RStudio MCP Server focuses on: ClaudeR - The Modern Researcher's Toolkit Connect a live R or Python session, in RStudio or Positron, to Claude Code, Codex, or any MCP-based LLM agent. Google Analytics MCP focuses on: 🌐 Live Documentation & Web Portal: https://ga4.builditwithai.xyz. Environment-variable extraction: ClaudeR - RStudio MCP Server yielded no variables extracted; Google Analytics MCP yielded 4 variables.
What each one does
ClaudeR - RStudio MCP Server
ClaudeR - The Modern Researcher's Toolkit Connect a live R or Python session, in RStudio or Positron, to Claude Code, Codex, or any MCP-based LLM agent. For interactive analysis, multi-agent orchestration, and automated manuscript auditing. It runs locally over stdio via the published package.
From the project's README.
Google Analytics MCP
🌐 Live Documentation & Web Portal: https://ga4.builditwithai.xyz. It runs locally over stdio via the published package.
From the project's README.
14 signals, side by side
| Signal | ClaudeR - RStudio MCP Server | Google Analytics MCP |
|---|---|---|
| Monthly installs | 11,271 | 12,229 |
| GitHub stars | 342 | 242 |
| Last commit | 2026-09-15 | 2026-09-17 |
| Releases · last 90 days | 2 | 10+ |
| In the registry since | Feb 2026 | Sep 2025 |
| Registry versions | 13 | 6 |
| Documented tools | not extracted - see README | not extracted - see README |
| Env vars extracted | none extracted - see README | 4 |
| Runs | local (stdio) | local (stdio) |
| Endpoint auth | local only | local only |
| Maintenance | actively maintained | actively maintained |
| License | NOASSERTION | MIT |
| Language | Python | Python |
| Category rank | #2 of 81 | #4 of 81 |
Environment variables found in the READMEs
These are extracted names, not a requiredness check. Project docs may mark them optional or require other setup.
ClaudeR - RStudio MCP Server
No environment variables were extracted from the README setup. Check the project documentation for other authentication or configuration steps.
Google Analytics MCP
- GA4_PROPERTY_ID
- GOOGLE_APPLICATION_CREDENTIALS
- DO_NOT_TRACK
- MCP_TELEMETRY_OPT_OUT
Which one, for what
Derived from the signals above, not hands-on testing.
Pick ClaudeR - RStudio MCP Server if…
- → category standing - #2 of 81 maintained data & analytics servers
Pick Google Analytics MCP if…
- → release velocity matters - 10+ releases in 90 days vs 2
- → clear licensing matters - MIT vs undeclared
Who's behind them
ClaudeR - RStudio MCP Server - imnmv: 1 MCP server tracked, 1 maintained, 342 combined stars.
Google Analytics MCP - surendranb: 2 MCP servers tracked, 2 maintained, 247 combined stars. Full record.
Not sold on either?
The next-ranked data & analytics servers we track:
Quick answers
Is ClaudeR - RStudio MCP Server better than Google Analytics MCP?
The numbers don't separate them: 11,271 vs 12,229 monthly installs, and both committed within the last month. Choose on fit - execution model, extracted environment variables, endpoint authentication, and license are in the table above. Data as of 2026-09-18.
Can I use ClaudeR - RStudio MCP Server and Google Analytics MCP together?
Yes - MCP clients accept multiple servers in one config, so you can enable both data & analytics servers side by side. If their tools overlap, keep the one whose toolset fits to keep your agent's tool list lean.
What environment variables do their READMEs document?
ClaudeR - RStudio MCP Server: no environment variables extracted from the README setup; Google Analytics MCP: 4 environment variables extracted from the README setup. Extraction does not rule out other authentication or configuration steps; check each project's current documentation.
Keep exploring: best data & analytics servers · Python servers · all comparisons · every server
Methodology: package-usage signal = npm/PyPI installs (last month, platform APIs) · maintenance = commit recency + release cadence (GitHub) · tool lists and environment-variable names extracted from each project's README · endpoint auth from our own nightly probes. We haven't hand-tested these servers; everything here is data as of 2026-09-18.