"Semantic UI" matching MCP servers:

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.

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 read-only MCP server that reports BI pipeline readiness, blockers, and the next allowed action for governed Power BI workflows. It never writes files, executes warehouse work, or grants human approvals.

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

MCP server that diagnoses Apache Spark job failures and optimizes performance using stack-trace analysis and LLM providers, supporting EMR and local sources.

Enables LLM agents to query governed GA4 metrics over MCP, returning definitions, assumptions, and failure modes from a MetricFlow semantic layer without writing SQL.

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.

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.

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.

Provides AI assistants with semantic layer visibility and multi-dimensional querying capabilities over Cube.js data.

Enables discovery of Power BI workspaces and semantic models via REST API and Microsoft Modeling MCP, with authentication options including service principal and device login.

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.

A read-only MCP server that exposes dbt project artifacts and data quality result tables (BigQuery/Postgres) to LLM clients, enabling deep introspection, run-history analysis, source freshness, test coverage, and lineage walks.

Enables AI agents to safely describe, query, and compute metrics across SQL databases using open-format semantic models, custom context, and skills, with read-only and dialect-checked execution.

An MCP server that enables advanced CSV analysis and data visualization using Google's Gemini AI and Plotly. It allows users to perform exploratory data analysis, generate interactive charts, and conduct complex reasoning on tabular data.

An MCP server that exposes VeloDB analytics data to AI clients through a governed semantic metrics layer (MetricFlow) and raw SQL fallback, with multi-workspace isolation, staging workflows, and a web UI for managing models.

Enables LLM agents to interact with the MOSTLY AI Platform for generating synthetic data, including training generators, managing data connectors, executing queries, and generating synthetic datasets with OAuth 2.1 authentication.

Read-only MCP server for FP&A & management reporting — governed metrics, financial statements and drill-down over a SQL semantic layer. Your own financials on your own warehouse, not market data. Open core of Précis. (metric engine · ClickHouse · OIDC · Docker)

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

Enables AI agents to import and refresh product feeds, edit products, write rules, run optimization and audits, connect ad channels, set performance labels, and create product ad creatives.