Tableau Knowledge MCP
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- AlicenseAqualityAmaintenanceEnables querying Tableau lineage through the Metadata API via MCP, supporting impact analysis, where-used lookups, and content search. Provides read-only tools for answering natural-language questions about workbooks, datasources, fields, and dependencies.721 PyPI1MIT
- FlicenseNot gradedqualityCmaintenanceProvides MCP tools to query Tableau Server/Cloud datasources via REST API and VizQL Data Service, with support for Gemini or OpenAI as the LLM backend. Enables a natural language chat interface that can be embedded in Tableau dashboards, automatically including dashboard filter context.-
- AlicenseNot gradedqualityCmaintenanceEnables people and AI agents to query local code repositories as graphs through stdio MCP tools for project listing, search, dependency tracing, impact analysis, bridge detection, node inspection, bounded code reading, and optional LSP definition/reference lookups.MIT
- FlicenseBqualityBmaintenanceA production-grade MCP server that exposes Tableau Server/Cloud as a BI platform, enabling project, workbook, data source, user, group, job, lineage, and export operations via natural language, with role-based permissions and token optimization.69-
- AlicenseNot gradedqualityBmaintenanceEnables querying a Neo4j knowledge graph built from Git repositories and relational database schemas via MCP, with tools for schema inspection, Cypher queries, and searching code and database entities.MIT
- FlicenseNot gradedqualityDmaintenanceMCP server that wraps the Tableau REST API and VizQL Data Service to expose tools for listing workbooks/datasources and querying datasources, enabling LLM agents to interact with Tableau Server/Cloud through natural language.-
TDQS
Scored across 23 tools
Most tools target clearly distinct resources or actions, and descriptions draw explicit boundaries between similar areas such as search_nodes vs get_node and lineage vs impact_analysis. The main mild overlap is in the exploration cluster (get_node, list_edges, lineage, impact_analysis), where an agent may need to read descriptions carefully to choose the right scope.
The set predominantly follows a readable snake_case verb_noun pattern such as list_sources, create_graph, search_nodes, and delete_semantic_context. A few noun-only exceptions like lineage, impact_analysis, server_info, graph_health, and ossie_guide are still clear but slightly break the pattern.
At 23 tools the surface is on the heavy side, but the domain is broad: graph/source management, node exploration, semantic-context CRUD, Ossie import/export/validation, agent testing, and preference checking. Each tool maps to a recognizable workflow rather than obvious filler, so the count is high but defensible.
Coverage is strong across the core lifecycle: server/graph discovery, graph creation and update, source listing/addition/removal, node search and inspection, lineage/impact, semantic-context search/create/update/delete, and Ossie workflows. The main minor gap is the absence of an explicit delete_graph operation, though the rest of the domain is well covered.