"SQL-Based Technologies or Concepts" matching MCP servers:

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.

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.

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.

A metadata-guided, read-only SQL analysis MCP server for DataHub that helps agents perform safe analytical queries using DataHub schema, lineage, ownership, and tags, with a deterministic planner and strict SQL safety layer against local synthetic SQLite, guaranteeing zero write or mutation operations.

Enables data-quality workflows by applying named, deterministic transformations to records and generating replayable lineage with content hashes, so users can verify that cleaned results follow exactly from raw inputs.

Enables MCP clients to discover schemas, keys, and lineage across bronze, silver, and gold tables, and to run guarded read-only SQL queries against the lakehouse.

An AI-driven data visualization and SQL chat platform that enables natural language to SQL conversion, interactive chart generation, and database querying through a comprehensive MCP interface. Supports multiple databases, intelligent chart recommendations, and secure data management with temporary link sharing.

This server enables agents to identify users and organizations, inspect versioned datasets and model lineage, start training jobs, run and read evaluations, verify deployments, and execute typed workflows through authenticated MCP endpoints.

Connects AI clients to the Standard Metrics API for automated analysis of venture capital portfolio data. It enables users to query financial metrics, track company performance, and generate comprehensive reports using natural language.

A read-only MCP server for querying SQL Server Analysis Services (multidimensional cubes via MDX and tabular models via DAX) using Windows Integrated Security.

An MCP server that provides safe, read-only access to a PostgreSQL warehouse, enabling schema introspection, guarded SQL queries, and truncated result reporting for LLMs.

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

Provides a stateful local Spark session for running PySpark and SQL cells, enabling local data exploration before deploying to Microsoft Fabric.

Enables language models and users to perform exact arithmetic, symbolic math, unit/date conversions, statistics, and read-only SQL queries over registered files, with every operation logged by a citable ID that humans can re-run.

A tool that converts natural-language requests into full, deployable, execution-verified Apache Flink SQL jobs by orchestrating an LLM to draft specs, generate Flink SQL, and verify correctness against real Flink clusters and streaming semantics—including windowed aggregates, joins, Kafka grounding, and Deployable pipelines via the generate_flink_job, ground_kafka_topic, and deploy_to_kafka tools.

Enables agents to query curated metrics through a registry-backed semantic layer, preventing ad-hoc SQL and enforcing consistent definitions. Every numeric claim is verified against the underlying query result before it is allowed to be sent.

Local MCP server for AI Model Manager that captures SQL data-model metadata and exposes it to MCP clients over stdio.

Enables AI assistants to interact with Hadoop Hue for executing SQL queries using Hive, SparkSQL, or Impala and managing HDFS files. It supports directory browsing, file transfers, and exporting query results to CSV through the Model Context Protocol.

Enables executing SQL queries, exploring database schemas, and exporting results from Redash via MCP, with ad-hoc queries and write protection.