SAS MCP Server
OfficialRelated Servers
Alternatives to SAS MCP Server
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityDmaintenanceExecutes Python code with safety constraints and manages Python packages through the Model Context Protocol.15 npm3MIT
- AlicenseNot gradedqualityDmaintenanceEnables secure cloud-based execution of code across 14+ programming languages within a sandboxed environment. It supports file management, standard input/output handling, and automatic generation of visual artifacts like plots and charts.MIT
- AlicenseNot gradedqualityAmaintenanceEnables secure remote command execution and bidirectional file transfers on SSH servers through the Model Context Protocol. It features robust security controls including command whitelisting, credential isolation, and support for multiple SSH connection profiles.980 npm926ISC
- AlicenseNot gradedqualityDmaintenanceEnables AI to execute RPA applications and workflows through the Model Context Protocol, supporting both local and cloud modes.10 npm1MIT
- AlicenseNot gradedqualityCmaintenanceEnables users to execute notebook cells, manage environments, and validate workbooks via MCP, facilitating reproducible machine-learning answers.MIT
- FlicenseNot gradedqualityDmaintenanceEnables secure execution of Python code, SQL queries, and metric fetching through MCP with ephemeral Docker sandboxing.-
TDQS
Scored across 92 tools
Given 92 tools, the descriptions do a strong job of drawing boundaries: search_glossary_terms vs list_glossary_terms, upload_data vs upload_inline_data vs upload_file, and list_castables vs list_source_tables vs list_compute_tables are each explicitly differentiated. A few near-pairs (e.g. get_castable_data vs query_data vs get_compute_table_data) require careful reading, but conflicts are generally resolvable from the text.
Names uniformly follow snake_case verb_noun (list_*, get_*, create_*, update_*, delete_*, catalog_*, *_glossary_term*), with consistent prefixes per subsystem. No camelCase or vague verbs anywhere.
92 tools is far past the practical ceiling for reliable selection, even accounting for the genuinely broad Viya surface (CAS, compute, reports, jobs, ML, rules, decisions, glossary). Several clusters (glossary term types, business rules vs rules, decision flows) push this into 25+ territory and risk misselection.
Coverage is unusually deep: full CRUD across glossary terms/types, business rules, decision flows, reports, jobs, and CAS/compute data, including lifecycle extras like revisions, publishing, and promotion to memory. Minor gaps remain (no report metadata rename/update, no per-object report edit), but these are small relative to the surface.