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Scored across 39 tools
Several tools have heavily overlapping purposes: get_analytical_metadata, get_analytical_model, and list_analytical_datasets all expose analytical metadata; get_relational_metadata, get_relational_entity_metadata, and list_relational_entities overlap similarly; and execute_query, query_analytical_data, query_relational_entity, and smart_query all perform querying. get_asset_details vs get_asset_by_compound_key and get_space_assets vs list_catalog_assets add further ambiguity. Despite detailed descriptions, an agent will frequently struggle to choose the correct tool.
The vast majority of tools follow a clear snake_case verb_noun pattern with consistent get_, list_, create_, update_, delete_, and query_ prefixes. Minor deviations exist, such as smart_query (adjective+noun) and phrasal names like find_assets_by_column and get_asset_by_compound_key, but there is no mixing of naming conventions or camelCase.
At 39 tools, the server is well above the 25+ threshold and feels heavily bloated. The count is inflated by near-duplicate metadata and query tools that could be consolidated into fewer, more coherent tools. While SAP Datasphere is a broad platform, this surface is not well-scoped for an MCP server.
The tool set covers discovery, metadata retrieval, querying, task execution/monitoring, and database-user lifecycle management fairly well, with no obvious dead ends in those workflows. However, it lacks lifecycle operations for spaces, assets, task chains, and connections, offering only list/run/get capabilities where create/update/delete would often be expected in a management-oriented server.