"sql server" matching MCP servers:

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

Enables AI assistants to execute read-only SQL against Spark, Hive, and HiveServer2-compatible clusters, discover databases and table schemas, and connect using multiple authentication modes.

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.

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

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.

Move data between Kafka, NATS, RabbitMQ, MQTT, IBM MQ, SQL databases, MongoDB, files and HTTP: publish messages and run copy routes with endpoints given inline as JSON, while the rows never pass through the model's context.

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 querying, inserting, and managing unstructured data in RawTree via SQL, JSON ingestion, and log inspection through MCP clients like Claude Code, Cursor, and Claude Desktop.

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

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.

This MCP server enables database migration between heterogeneous systems (Oracle, PostgreSQL, SQL Server, Netezza to PostgreSQL or SQL Server) through AI assistants by providing tools for command preview, execution, validation, and workflow suggestions.

Enables SQL lineage analysis, including table-level and column-level lineage, field-level tracing, and support for multiple SQL dialects via sqlglot.