rmcp
rmcp is a Python-based Model Context Protocol (MCP) server that exposes 52 statistical analysis tools (organized into categories) backed by a curated whitelist of CRAN R packages. It can be run locally (stdio for MCP clients such as Claude Desktop; optional HTTP transport for web apps) or used via a live HTTPS endpoint with interactive docs and health checks.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Strengths/indicators: HTTPS is implied for the live HTTP endpoints and an SSL mode is documented for local serving. Weaknesses/unknowns: no authentication/authorization is documented; the service likely executes R code/tooling against user-provided inputs, so input validation/sandboxing quality is critical but not described here. Dependency list shows common libraries and structured logging, but no CVE history or sandboxing details are included in the provided content.
⚡ Reliability
Best When
You want an MCP-compatible assistant to run R-backed statistical analyses interactively with conversational tool calls, optionally over HTTP for web clients.
Avoid When
You need strong access control, auditability per user, or documented rate limiting/quotas; or you must ensure strict compliance/data residency guarantees for sensitive data.
Use Cases
- • Regression and econometrics workflows (e.g., linear/logistic models, panel data, IV)
- • Time-series analysis and forecasting (e.g., ARIMA, stationarity testing)
- • Statistical testing and experimentation (e.g., t-tests, ANOVA, chi-square)
- • General data analysis and transformations (descriptives, correlation, standardization, winsorization)
- • Machine-learning modeling (e.g., clustering, trees, random forests)
- • Producing formatted statistical output and inline visualizations for analyst workflows
Not For
- • As a general-purpose data API without statistical tooling focus
- • Highly secure multi-tenant environments where per-user authentication/authorization is required (no auth described)
- • Workloads needing guaranteed numerical reproducibility across arbitrary R environments without pinning R/package versions
Interface
Authentication
No authentication/authorization mechanism is described in the provided README (for both local and live HTTP endpoints). This suggests the service may rely on network-level controls when used remotely.
Pricing
No pricing information is provided in the supplied content.
Agent Metadata
Known Gotchas
- ⚠ MCP tool calls may fail if required R or specific R packages are not installed; README indicates a 'check-r-packages' command but does not document failure recovery semantics.
- ⚠ Using the live hosted endpoint may introduce latency, resource limits, or environment variability (not documented in the provided content).
- ⚠ Statistical workflows can be sensitive to data schema/format; tool failures may require reformulating inputs rather than simple retries.
Alternatives
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Scores are editorial opinions as of 2026-03-30.