"How to query MySQL data on a server" matching MCP servers:

Enables fast, targeted queries against an Obsidian vault knowledge graph using tools like search, neighbor traversal, and pathfinding, without needing to load the full graph into LLM context.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

Enables querying and modifying a local MySQL database and a Neo4j knowledge graph through separate read-only and read-write tools, with permissions scoped by database user and Neo4j transaction mode. Lets clients run SELECT statements against MySQL, execute inserts/updates/deletes, and run read or write Cypher queries.

An MCP server that provides safe access to your iMessage database through MCP, enabling LLMs to query and analyze iMessage conversations with proper phone number validation and attachment handling

Provides a single-writer MCP server for a governance-grade knowledge base of markdown documents with version control and query capabilities.

Turns GitHub repository history into a cited maintainer skill for coding agents, providing tools to collect evidence, query the knowledge graph, and inspect bundles.

An MCP server that transforms natural language questions into SQL, executes queries on PostgreSQL, and returns human-friendly responses, with Redis memory for context.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Turn Obsidian notes into agent-ready context, preview-only plans, and safe repo handoffs.

On Board is a local MCP server that gives multiple AI agents and IDEs a shared project memory, ticket queue, and handoff history, so agents can seamlessly continue each other's work. It supports agent-to-agent wake events, enabling autonomous workflows like reject-fix-resubmit cycles without human relay.

Look up countries, timezones, periodic table elements, physical constants, units, HTTP status codes, and MIME types via MCP. STDIO or Streamable HTTP.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

Provides hybrid search over local markdown knowledge bases using BM25 keyword search and vector semantic search. Enables indexing, querying, and retrieving markdown documentation with dual-mode support for local and remote agents.

Provides personalized Data Engineering learning updates by fetching recent news about DE concepts, patterns, and technologies, while tracking user knowledge to present only new information relevant to their learning journey.

Provides AI clients with a governed, searchable catalog of go-to-market context, including ownership, measurement, lineage, runbooks, and bulk-change templates. Optionally integrates with the UTM Builder for governed UTM reference data, preview, and issuance.

Enables AI agents to maintain persistent, searchable two-layer memory with 37 tools, hybrid search, knowledge graphs, and enterprise features like authentication and backups.

A persistent memory layer for AI tools that decouples personal data from AI's unstable memory, enabling you to mention information once and have it remembered forever across all conversations.

Project management and session memory for AI agents that provides 13 tools for tracking projects, tasks, and context during AI coding sessions.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.