No user-submitted related servers found.
Scored across 65 tools
Most tools are clearly separated by resource type (measure, dimension, fact, fact_table, dimension_group, relationship, version, project, connection, git_connection, data_mart) and action (get/list/write/delete/bulk/import/export). A few potential confusions exist: df_get_rmd vs df_get_consolidated_rmd overlap heavily (one is a normalized subset of the other), and df_get_connection_schema vs df_get_connection with include_db_schema=true could be confused, but descriptions clarify the distinction.
All tools follow a consistent df_<verb>_<resource> pattern with snake_case throughout. Verbs are predictable: get/list for reads, write for create/replace/update, delete for removals, bulk_write for batch operations, and import/export for transfer operations. The prefix df_ is applied uniformly, making the set highly predictable.
65 tools is a very large surface for a single MCP server. While the domain is broad (semantic modeling, fact tables, relationships, versions, projects, git, access control, import/export), the count exceeds the 25+ threshold and will impose significant context overhead on agents. The tools could reasonably be split into multiple focused servers (e.g., semantic model editor, admin/access, git/import-export).
The tool surface is remarkably complete for the DataForge domain: full CRUD for projects, versions, measures, dimensions, facts, fact tables, dimension groups, relationships, verification filters, and git connections; plus bulk operations, assignment/detachment, access management, import/export with dry-run validation, and health/cache utilities. No obvious dead ends or missing lifecycle operations were identified.