"Vector Database" matching MCP servers:

Enables semantic code search across multi-language codebases using natural language queries, integrated with Qdrant vector database for fast, cached retrieval.

Enables AI agents to run deterministic database intelligence tasks including natural-language-to-SQL synthesis, AST SQL auditing and optimization, schema migration diffing, SQL transpilation, index and normalization advice, PII scrubbing, fixture generation, connection-pool sizing, isolation-hazard auditing, and ORM model compilation over Streamable HTTP or stdio.

MCP server with local vector search for your codebase. Smart indexing, semantic search, Git history — all offline.

Provides semantic vector search over local codebases via MCP, enabling hybrid search (dense + sparse + RRF) for any MCP client like GitHub Copilot or Claude Desktop.

Enables AI coding agents to preflight SQL, NoSQL, graph, warehouse, and vector queries against real schemas or pasted DDL, estimating cost/risk, suggesting verified rewrites, and explaining plans before execution. It supports 12 database engines and catches costly query patterns without running the query.

Enables AI agents to format SQL, explain queries in plain English, analyze schemas, build queries from natural language, and generate migrations, all without requiring a database connection.

Enables secure read-only querying of multiple database types (MySQL, PostgreSQL, MSSQL, Oracle) through natural language. Automatically reads database configuration from project files and blocks any data modification operations for safety.

Converts vector PDF circuit schematics into evidence-preserving SchematicIR JSON with tools to inspect, convert, and validate schematic PDFs.

Offline PostgreSQL diagnostics: reviews SQL, risky migrations, candidate indexes and EXPLAIN JSON plans without database credentials or executing statements.

Provides comprehensive MySQL database operations including CRUD, performance optimization, health analysis, and anomaly detection. Supports multiple connection modes, OAuth2.0 authentication, and role-based permissions for database management through natural language.

A high-performance MCP server for semantic search and codebase indexing using the Qdrant vector database. It features optimized embedding pipelines, AST-aware chunking, and git metadata enrichment for fast, privacy-focused local or remote search.

Enables semantic and hybrid search over documents and codebases backed by a Qdrant vector database, with collection management, document CRUD, metadata filtering, and AST-aware code indexing using local or cloud embedding providers. Supports natural language queries, incremental re-indexing of changed files, and custom configurable prompts for guided workflows.

A server component of the Model Context Protocol that provides intelligent analysis of codebases using vector search and machine learning to understand code patterns, architectural decisions, and documentation.

A comprehensive MCP server that provides a graph database for tracking software codebase components, their relationships, and associated tasks/goals.

Enables AI agents to perform hybrid code search, get explanations, analyze relations and impacts, retrieve context packs, and generate documentation across ~45 languages via 17 MCP tools, all powered by a local vector database and LLM.

Enables VS Code Copilot to understand and query codebases through semantic search, call graph analysis, and related file discovery, using a local SQLite index with graph and vector embeddings.

Indexes Unity projects into a SQLite database and exposes 23 MCP tools for Claude to explore scenes, prefabs, scripts, and assets via structured queries.

Embeds your codebase into a local vector and graph database and exposes it as an MCP tool, enabling AI assistants to perform fast semantic search over your code using Graph RAG.

Enables AI agents to architect, validate, scaffold, and generate code for WTinyDB document database models and patterns.

Semantic search server for code and documentation using Qdrant vector database. Supports multi-language indexing, live updates, and natural language queries.

Semantic code search engine for Claude Code with hybrid search combining vector, FTS, AST graph, and ripgrep regex, with RRF fusion and reranking.

Headless MCP server for IDA Pro enabling on-demand database loading and programmatic reverse engineering workflows via MCP tools.

Enables AI agents to safely inspect Oracle Database schemas, objects, and PL/SQL source with a strict read-only guard, without ever modifying data.

Enables natural language queries about JavaScript/TypeScript codebases using graph and vector search. Allows finding functions by semantic description, callers, callees, and function details.

MCP server for semantic code search and dependency graph analysis. Indexes codebases into a knowledge graph with vector embeddings for AI-powered code understanding.

Four-layer hybrid search and knowledge graph for AI coding assistants: BM25 + vector embeddings + RAPTOR directory summaries + graph expansion fused into a single MCP tool.

Transforms codebases into a living knowledge graph with AI-powered code analysis, security scanning, and persistent semantic memory, leveraging Oracle 26ai vector and property graph capabilities.

Model-agnostic code memory MCP server using budget-aware graph retrieval for AI agents, providing sub-millisecond queries, token budgeting, and deterministic results without embeddings or vector databases.

Indexes project source files into a local Zvec vector database and provides semantic search through MCP tools, enabling natural-language queries over code.

140+ T-SQL static code analysis rules for SQL Database Projects