"Hybrid Memory Models Combining Relational, Graph, and RAG Approaches" matching MCP servers:

Enables hybrid retrieval-augmented generation with semantic and keyword search over documents, supporting multiple formats and providing MCP integration for Claude Desktop.

Enables hybrid search and agentic retrieval over Confluence pages via MCP tools, allowing Claude Desktop, Cursor, or Claude Code to search and retrieve Confluence content.

Enables cited, revision-aware retrieval over real IETF RFC chunks with zero LLM in the answer path, including graph-based supersession resolution and relationship queries via MCP tools.

Provides read-only MCP tools for hybrid semantic and keyword search over locally indexed PDF documentation, with citations and context retrieval for LLM agents.

Standalone MCP server exposing agentic-RAG tools (hybrid search, article retrieval, category listing) over a pgvector-backed knowledge base with bearer-token auth, designed for deployment on Render.

A RAG MCP server that enables retrieval-augmented question answering over PyTorch documentation, with hybrid search (FAISS+Chroma, BM25) and source-cited responses from DeepSeek.

Helps construct and validate Microsoft Graph REST API calls with on-demand tool loading, providing request details, required permissions, and code examples for categories like users, files, mail, calendar, groups, notes, tasks, sites, and subscriptions.

Provides read-only retrieval of STM32 datasheet and reference manual content with exact citations, page numbers, register details, and source hashes so Claude can answer questions or generate code from verified evidence.

Transforms static gemini-cli documentation into a queryable RAG service, enabling developers to ask questions about Gemini CLI in natural language and receive instant, accurate answers based on the official documentation directly within their workflow.

A Model Context Protocol (MCP) server with Retrieval-Augmented Generation (RAG) for answering questions about imaginary SuperNova documentation. Enables semantic search over documentation using HuggingFace embeddings.

MCP server for RAG-based search over 1C Enterprise configuration documentation, enabling natural language queries to find objects like справочники, документы, and отчеты.

MCP server for querying versioned C2PA specification knowledge graphs, enabling AI agents to browse entity definitions, validation rules, and version diffs via natural language.

Enables MCP clients to semantically search a local folder of markdown documents via a single search_notes tool, with embeddings generated locally by Ollama and support for excluding superseded or archived material.

Enables AI agents to query organizational architecture and governance constraints, returning evidence-grounded answers from documented structures.

Enables querying Confluence or Kubernetes documentation through hybrid search and an agentic RAG pipeline, returning structured answers with citations.

A local MCP server that accumulates the business-domain knowledge of a software project — flows, integrations, decisions, nuances — and makes it available to any MCP-compatible agent (Claude Code, Cursor, Copilot, Gemini, OpenCode). The goal is to capture the why behind the code: the stuff a new developer would need six months from now to understand why a thing is built the way it is. The what is

MCP server exposing a RAG knowledge base as read-only tools (search_knowledge_base, ask_knowledge_base, kb_stats, kb_diagnostic) for AI clients like Claude Desktop and Cursor, enabling token-efficient document retrieval and Q&A.

An MCP server that enables users to query Kedro framework documentation using retrieval-augmented generation. It builds a local knowledge base from documentation files to help users navigate and apply Kedro's data science pipeline framework.

This MCP server provides a deterministic index of the First Principles Framework (FPF) spec, enabling agents to query patterns, routes, and relations without vector databases.

Retrieval-Augmented Generation system serving LangGraph documentation through the Model Context Protocol, enabling semantic search and context-aware responses.