r-coding-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| detect_workflowC | Detect the R workflow type from a directory path |
| validate_projectA | Validate an R project against best practices. Optional filters narrow the returned findings; the summary always counts all findings. If no project type is detected the result has no findings and a |
| validate_fileA | Validate a single R or Quarto file. Optional filters narrow the returned findings; the summary always counts all findings. |
| get_practiceC | Get details about a specific best practice |
| list_practicesC | List best practices, optionally filtered by workflow, category, minimum severity, tags, enforcement type (automated check or guidance only) or free-text query |
| generate_templateC | Generate a project template for a specific R workflow |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 6 tools
Each tool has a clearly distinct target: detect_workflow (identify type), validate_file vs validate_project (different scopes of validation), get_practice vs list_practices (single vs collection), and generate_template. No two tools could reasonably be confused for one another.
All six tools follow a consistent verb_noun snake_case convention (detect_workflow, validate_file, validate_project, get_practice, list_practices, generate_template). The pattern is predictable throughout with no deviations.
Six tools is well-scoped for an R best-practices/validation server, with no redundancy. Each tool covers a distinct capability (detection, validation, practice lookup, template generation).
The surface covers the core lifecycle: detection, validation at file and project level, practice discovery, and template generation. Some gaps remain, such as auto-fix/remediation of findings, but the essential workflows are covered.