"Semantic search, RAG, and memory systems" matching MCP servers:

Enables AI agents to store, search, and recall semantic memories with three memory types (semantic, episodic, procedural) and auto-consolidation, compounding intelligence over time.

A lightweight MCP server for querying arXiv with semantic and keyword search, supporting natural language queries and structured filters.

Enables semantic search over documentation stored in a Vectorize database using the Cloudflare Agents SDK, allowing users to query indexed content via natural language.

Provides AI coding agents with structured access to indexed codebases via semantic search, symbol analysis, and file reading tools.

A stateless MCP server for composing, validating, auditing, and rendering consequential documents from a small semantic model.

A Python MCP server that enables semantic search through Search Labs blog posts indexed in Elasticsearch, allowing Claude to intelligently retrieve relevant information from the blog content.

MCP server for Google's Gemini File Search (RAG). Manage file search stores, upload documents, and query with RAG through 12 tools.

Enables AI-powered access to authoritative design systems knowledge, including W3C standards, WCAG guidelines, and best practices from 188+ curated entries via semantic vector search.

MCP server for querying the Semantic Scholar Academic Graph API, enabling paper search, author lookup, citation tracking, and paper recommendations.

Enables coding agents to retrieve project context from an AnythingLLM RAG at session start and write back human-approved durable facts via five workflow-shaped tools.

An MCP server that provides access to Semantic Scholar's academic paper database, enabling paper search, detailed retrieval, author info, and citation export.

Provides a semantic intelligence layer for SQL Server databases, enabling AI agents to understand schema structure, relationships, and safely interact through policy-gated tools.

Provides web search and page fetching capabilities using Ollama's API.

Enables any MCP-capable LLM client to search self-hosted long-term memory over markdown and PDF documents, combining dense semantic vectors with BM25 keyword retrieval and optional cross-encoder reranking. Exposes a read-only tool surface for querying incidents, runbooks, and other knowledge-base content, while writes happen out-of-band through ingestion jobs or a token-gated internal API.

Enables semantic control and monitoring of openHAB smart home items through natural language, supporting commands, queries, and time-based monitoring tasks.

Enables LLM clients to analyze numerical data series (NumPy, Pandas, Polars, lists) and receive token-efficient natural-language summaries of trend, volatility, anomalies, seasonality, and data quality instead of raw values. Supports single-series analysis, batch processing of multiple series, and structured JSON output.

Enables Claude Desktop and Claude Code to answer questions about Indian mutual funds from official SEBI, AMC, and AMFI documents by retrieving cited, layout-aware document chunks through hybrid semantic and keyword search.

Access millions of academic papers from Semantic Scholar using the Model Context Protocol, with tools for paper search, author analysis, and research organization.

Indexes PDF documents into Qdrant and exposes semantic search as MCP tools, enabling RAG-based interactions with your documents.

Enables semantic search across documentation stored in Gemini FileSearchStores, returning AI-generated answers with source citations.

MCP server that integrates Apache Ranger authorization with RAG, enforcing Ranger policies to control access to knowledge bases before forwarding queries to RAG Studio.

Dynamic semantic memory for AI assistants that persists across sessions and clients, enabling natural language recall and project-scoped isolation.

Enables users to search Reddit by keyword or subreddit, read thread URLs with comments, filter and sort results, and receive flat post and comment rows without needing a Reddit login or API key.

Enables AI agents to query verified-emissions knowledge graphs in natural language and validate data against supported standards, with metered billing and optional verified identity.

Enables LLMs to search and retrieve Autotask tickets, companies, and contacts using advanced semantic search and AI-powered reranking.

Enhanced MCP knowledge graph memory server with cloud persistence and semantic search, acting as a drop-in replacement for the standard memory server.

Enables AI agents to search the web with clean page text, crawl websites into Markdown, extract articles and structured data, and monitor Google News and press releases for LLM context and RAG pipelines. It works with MCP clients and uses the user's own Apify API token for pay-per-result data retrieval.

This MCP wraps Bitrefill public API to allow agents to search for products and shop using cryptocurrencies like Bitcoin, Ethereum, Solana, and many more.

MCP server for Cloudflare D1 database introspection, providing schema analysis, relationship extraction, validation, and optimization suggestions via Semantic Intent patterns.

Pay-per-use semantic web search for AI agents. Powered by SearxNG, agents pay in sats via Lightning Network micropayments — no API keys required.