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

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

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.

MCP server that scans PyPI packages and GitHub repos for security vulnerabilities, dangerous patterns, and prompt injection vectors, providing trust scores (0-10) and side-by-side comparisons.

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.

A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.

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

Enables natural-language semantic search of a project's code through a local vector index, so questions that plain grep cannot match return ranked file:line references. It also indexes or refreshes projects on demand, reports index status, and optionally flags near-identical code across files.

Provides semantic code search over codebases using local embeddings with natural language queries. Supports hybrid search, file watching, and respects .gitignore.

A self-hosted semantic code retrieval MCP server that enables natural language queries to find relevant code snippets using Tree-sitter AST parsing, embedding, and hybrid search with reranking.

Enables coding agents to search locally indexed repositories with hybrid semantic and lexical retrieval, returning exact source citations with file paths and line ranges.

Enables AI agents to perform semantic search over codebases by converting natural language queries into efficient search patterns like grep and ripgrep. It utilizes LLMs to verify relevance and find code snippets that traditional keyword-based searches might miss.

Enables AI coding agents to perform semantic code search locally, finding code by meaning rather than exact keywords.

Provides structured semantic context for JavaScript/TypeScript codebases, enabling coding agents to navigate, review, and change code with explicit uncertainty.

An MCP server over stdio that lets agents find code by meaning using local Ollama embeddings and trace callers or callees through real tree-sitter AST parsing rather than regex or keyword matching. It shares the same search and trace engine as the companion CLI, so MCP-native agents and shell-only agents get identical, reproducible results.

Provides worktree-aware semantic code indexing and search via MCP, enabling agents to query indexed code and monitor indexing status while keeping lifecycle operations separate.

Enables offline analysis of MySQL slow query logs, individual SQL statements, and MyBatis mappers to produce deterministic index recommendations and migration SQL without requiring an API key.

Enables semantic code search over local repositories, providing tools like semantic_search and list_indexed_repos to Claude Code, so users can find relevant code sections via natural language instead of grep.

Builds a semantic knowledge graph of C++ code and exposes 9 MCP tools for AI assistants to search classes, functions, inheritance, callers, callees, overrides, and more.

An MCP server designed for SQL analysis that demonstrates sampling capabilities by interacting with SQL files in a local directory. It provides a framework for testing client-side sampling while managing SQL-based contexts.