"oracle" matching MCP servers:

Enables Claude Code agents to look up curated per-library documentation domains, GitHub repositories, and personal notes before reading docs or source, so library questions are answered from trusted references instead of blind web searches.

Magic: The Gathering MCP server with card search, rules lookup, deck analysis, and Commander intelligence

Hearthstone MCP server with card search, deck analysis, and strategy coaching. Gives LLMs access to every Hearthstone card plus built-in strategy knowledge for deck building and gameplay advice.

MCP server for semantic search over Oracle philosophy using hybrid search (FTS5 + LanceDB). Integrates with Claude Code for querying wisdom and managing a knowledge vault.

Enables semantic search and knowledge management for storing and querying principles, patterns, and learnings using hybrid keyword and vector search.

An MCP server that gives Claude persistent, locally-stored memory with verification-gated lessons and a Pattern Oracle to prevent unverified guesses from becoming ingrained facts.

A local MCP server for maintaining internal consistency in fiction writing, with constraint-based fact checking, branching, and author-oracle conflict resolution.

File-backed Memory MCP Server for multi-agent coordination, enabling AI coding agents to persistently store and search memories using JSON files without external services.

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.

A multi-vendor database management tool that enables LLMs to query, migrate, and synchronize schemas across SQL engines like PostgreSQL, MySQL, and Oracle. It features persistent knowledge management for storing AI-generated context about database structures and business rules.

Indexes codebases cut at function and class boundaries into Oracle AI Database 26ai, letting AI coding tools search code by name or meaning, trace callers, callees and change impact, read per-module wiki pages, and recall facts from earlier sessions. All of it is served over the Model Context Protocol with embeddings computed in-database, so no source code leaves the machine and no API key is required.

Enables storing and retrieving information using semantic search with Qdrant vector database. Acts as a memory layer for LLMs to persistently store and semantically search through information and metadata.