sas-mcp
Related Servers
Alternatives to sas-mcp
No user-submitted related servers found.
Related Servers
- FlicenseNot gradedqualityAmaintenanceEnables LLMs to interact with local Python environments, execute code and scripts, manage packages, and read or write files within an isolated working directory.-
- FlicenseNot gradedqualityAmaintenanceEnables LLMs to work with local Python environments by listing environments and installed packages, installing dependencies, and running code or scripts. Also supports reading, writing, and listing files within a sandboxed working directory.-
- FlicenseNot gradedqualityAmaintenanceEnables LLMs to run Python code and scripts across available environments, manage packages, and read/write files within a secured working directory. It also provides prompts and resources for common Python development tasks.-
- FlicenseNot gradedqualityAmaintenanceEnables LLMs to manage Python environments and packages, execute Python code or scripts, and read or write files within a secured working directory. It also provides prompts for common Python tasks such as function creation, refactoring, and debugging.-
- AlicenseNot gradedqualityCmaintenanceConnects AI agents to a local Stata installation so they can run Stata commands and .do files, inspect datasets and codebooks, export graphs, look up help, and verify stored r()/e() results with structured error reporting.AGPL 3.0
- FlicenseNot gradedqualityCmaintenanceEnables coding agents to execute Python code, run script files, and install pip packages locally via MCP.-
TDQS
Scored across 16 tools
Most tools target a distinct resource and action: config selection, code execution, dataset metadata, file transfer, and comparison. A few overlapping boundaries exist—session_status overlaps with list_libraries/list_datasets for a quick overview, and run_sas vs. run_sas_tests both submit code—but the descriptions make the intended use clear.
The majority follow a clear verb_noun pattern such as list_datasets, describe_dataset, compare_datasets, and download_from_sas. The exceptions are session_status and sas_doctor, which are noun-led and break the otherwise predictable scheme.
Sixteen tools is slightly above the typical 3–15 sweet spot, but each tool maps to a distinct part of the SAS workflow: connection management, session state, code execution, file transfer, metadata inspection, and validation. The count feels justified rather than bloated.
The surface covers the core SAS lifecycle well: configure/connect, inspect session and datasets, run code, retrieve logs, transfer files, compare tables, and run tests. Minor gaps exist—no explicit disconnect tool or direct file deletion—but these do not create dead ends since run_sas and session management cover most of them.