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

A general-purpose MCP server that lets AI work with multiple databases within clear boundaries.

Analyzes SQL queries for performance issues, provides optimization suggestions with automated rewriting, and recommends indexes across multiple database dialects (PostgreSQL, MySQL, Oracle, SQL Server).

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.

Verifies AI agent completion claims by checking that claimed output files exist and are fresh, acting as a lightweight physical telemetry gate.

Semantic React/TSX analysis MCP server that saves 70-90% tokens by using AST to retrieve precise component usages, prop flow, and state update information for AI coding assistants.

Formats SQL queries with support for 14 dialects, keyword casing, and tab width.

An MCP server for Dart AI task management that enables bulk operations using DartQL selectors to minimize token usage and context rot. It provides tools for batch updates, task and document CRUD, and safe CSV imports with integrated dry-run capabilities.

Provides a semantic intelligence layer for SQL Server databases, enabling AI agents to understand schema structure, relationships, and safely interact through policy-gated tools.

Enables a single Antigravity IDE to explicitly drive multiple isolated Antigravity CLI workers, choosing each worker's account, model, skills, task, and expected outputs while the bridge executes those decisions over STDIO with SQLite-backed task state. It returns bounded logs, cancellable task control, and immutable artifact exports whose chunks and whole files can be verified by SHA-256.

A Model Context Protocol server that provides AI assistants with comprehensive access to SQL databases, enabling schema inspection, query execution, and database operations with enterprise-grade security.

A Model Context Protocol server for interacting with MSSQL and PostgreSQL databases, offering tools for schema exploration and SQL execution. It features configurable query modes for safety and supports advanced authentication methods like Windows Auth and SSL.

Enables read-only interaction with SQL databases through MCP, providing database metadata exploration, sample data retrieval, and secure query execution. Supports MySQL with multiple transport options and built-in security features including SQL injection protection and data sanitization.

Token-efficient Figma MCP server with image export, query DSL, and design token support.

NestJS-based MCP server for MySQL with session-level database selection and SQL safety controls. Enables executing SQL queries, listing databases, and connecting MySQL users with configurable safe modes.

Provides secure, read-only access to Microsoft SQL Server with multi-layer protection, enabling safe query execution, schema discovery, and SQL script analysis through natural language.

Reduces token consumption by over 80% through intelligent file caching, returning only diffs for modified files and suppressing unchanged content. It features a suite of 12 tools for semantic search, batch reading, and efficient file editing to optimize LLM interactions with large codebases.

Enables MCP clients such as GitHub Copilot, Codex, Claude... to explore a SQL Server 2017+ database read-only: databases, schemas, tables, views, procedures, functions, indexes and dependency graphs, plus filtered, grouped, sampled and paginated queries capped by row limits.

MCP server that integrates sql-sop SQL linter into LLM clients, enabling linting SQL queries and listing lint rules via chat tools.

A read-only MCP server for browsing and querying SQL Server databases, providing tools to list schemas, tables, describe columns, and execute safe SELECT queries with validated parameters.

Runs SQL statements in memory against real PostgreSQL 18 and SQLite 3.49 engines after loading your schema and sample rows, returning each statement's own verdict: errors with line, column, and hints, constraint violations, or the resulting rows. It also compares both dialects to expose portability differences, all with no database server, network, or API key.