"Semantic layer for SQL query execution with dimensions and measures" matching MCP servers:

MCP server for the OrionBelt Semantic Layer. Enables LLMs to explore governed semantic models, compile YAML metric definitions into optimized SQL across 8 engines (BigQuery, ClickHouse, Databricks, Dremio, DuckDB, MySQL, PostgreSQL, Snowflake), and execute analytics queries via natural language. Works with Claude, Cursor, Windsurf, and Copilot.

A server that enables querying the dbt Semantic Layer through natural language conversations with Claude Desktop and other AI assistants, allowing users to discover metrics, create queries, analyze data, and visualize results.

Enables agents to interact with a governed semantic layer for querying and authoring metrics, providing tools for discovery, planning, validation, and execution of analytics queries.

Exposes a governed semantic layer to AI agents so they select metrics and dimensions from a closed, engineer-declared vocabulary instead of writing SQL. Validates every typed call against the contract, rejects out-of-contract requests with structured errors and suggestions, and returns query rows together with the compiled SQL and dbt lineage for auditing.

An MCP server that gives models semantic context about a data warehouse, including table meaning, units, lineage, and health, to reduce guessing.

Provides tools for named entity extraction, Wikidata disambiguation, sameAs schema linking, and knowledge graph triple construction, enabling structured semantic data processing.

MCP server for connecting to Microsoft Fabric and Power BI semantic models, enabling workspace browsing, dataset management, TMSL retrieval, DAX queries, model creation/editing, and includes a Best Practice Analyzer with 71 rules.

A versatile tool that enables querying and exporting data from multiple relational databases (MySQL, PostgreSQL, Oracle, SQLite, etc.) in read-only mode for data safety.

Enables secure interaction with multiple SQL database engines (MySQL, PostgreSQL, SQLite, SQL Server) through a standardized interface. Supports schema inspection, safe query execution, and controlled write operations with built-in security restrictions.

Executes SQL queries via ConnectorX and streams results to CSV or Parquet files, supporting multiple databases and optional token counting for CSV output.

Enables AI assistants to interact with SQL Server databases through tools for connectivity, schema exploration, SQL queries, and OEE metrics analysis.

A production-ready MCP agent that helps data teams define, validate, and visualize semantic metrics with enterprise-grade persistence, trust scoring, and BI integrations.

Paid remote MCP endpoint for AI SDK data queries, offering structured JSON tools, token-based access, usage receipts, and audit-ready workflow evidence.

50 tools and 400 functions for working with Excel/.xlsx spreadsheets — read/write, recalculate formulas, diff, repair broken references, and audit. Built for AI agents.

Provides a semantic gateway for AI agents to interact with the DataForge Product API, enabling the retrieval of projects, versions, measures, and dimensions. It features automated normalization and file-based caching to streamline access to DataForge metadata through the MCP protocol.

Provides read-only Microsoft SQL Server database access to AI agents with row-level security, enabling SELECT queries, table metadata, and schema introspection through MCP.

A fast, safe MCP server for YNAB providing read-only budget access by default, with 28 tools for budgets, transactions, categories, payees, accounts, scheduled transactions, and server-computed analytics.

MCP server that connects AI assistants to Redash, enabling listing of assets, read-only query execution, dashboard inspection, and alert management through the Redash API.

Enables data teams to define and validate metrics through conversation, inspect trust scores and lineage, and export definitions to Looker, Tableau, and dbt.

Enables AI agents to list metrics and dimensions, inspect definitions, and run governed metric queries against Apache Iceberg tables through a semantic layer, without writing SQL or accessing raw rows or columns outside their role.