358 published MCP servers in this category · page 1 of 6 · all categories
Vector search and retrieval MCP servers connect assistants to indexed collections and semantic search systems. They can retrieve relevant passages or manage a supported vector store. Retrieval-augmented generation uses these results as context, while the connected model remains responsible for composing an answer.
Compare filters, document identifiers, embedding requirements and source links. Check update and deletion operations when the underlying content changes. A similarity score indicates a match under the retrieval system’s method; it does not establish that the retrieved information is correct or current.
Browse the servers below for available tools, connection options and links to the maintainer’s documentation. A server can belong to several categories when it supports several kinds of work.
Official Redis server — search keys, inspect data structures and manage caches in natural language.
Enables AI agents and LLMs to interact with the web and extract information from web pages via the RAG Web Browser Actor.
Enables semantic search and retrieval of documentation using a vector database, allowing users to add and query documentation from URLs or local
Augments AI responses with relevant documentation context through vector search.
Augment AI responses with context by retrieving and processing documentation through vector search.
Enables semantic analysis of chat conversations through vector embeddings and knowledge graphs.
Provides LLM coding agents with vector search capabilities for software projects via the Message Control Protocol (MCP).
Enables semantic search in Qdrant vector databases using OpenAI embeddings.
Downloads websites and their assets for use with Retrieval-Augmented Generation (RAG) systems.
Integrates Qdrant vector database with an MCP server to store and retrieve code snippets.
Demonstrates a Retrieval-Augmented Generation (RAG) application integrated with MCP server.
MCP server with 36 enterprise tools: vector embeddings, CloudFormation/DynamoDB generation, OAuth/IAM/MFA validation, error analysis, data lake
Check observed content-use declarations before AI input, RAG indexing, training or search. AcqPath returns signed evidence of observed RSL declarations for supported URLs. It does not grant a…
12 pay-per-event data APIs: Maps, TikTok, Instagram, YouTube, LinkedIn, RAG, reviews, fuel, AEO.
Elite Obsidian RAG System - Second Brain with Claude Code Integration
AegnticMCP automates the creation and management of MCP servers, ensuring they are stable, adaptable, and intelligent.
The WAF for agents. Pattern-based + heuristic firewall scans prompts, RAG documents, tool argume...
AgentNull: AI System Security Threat Catalog + Proof-of-Concepts. Collection of PoCs for using Agents, MCP, and RAG in bad ways.
LLM-ready web search + instant answers + URL-to-clean-text fetch for agents and RAG.
Agentset MCP Server - Build RAG with Agentic superpowers
A powerful Model Context Protocol (MCP) server implementation with integrations for GitLab, Jira, Confluence, YouTube, and more. This server provides
Generate 18 AI readiness files (llms.txt, ai.txt, RAG indexes, schema) for any website.
Author rules from policy docs, then decide: a Rete engine gives the verdict, an LLM explains why.
AI 與科技名詞白話解釋：MCP、RAG、AI 代理人等名詞的白話定義。台灣繁體中文 MCP 工具。
Korean crypto × AI media MCP — channel stance, daily briefs, RAG, canonical store, AI personas.
Lineage-gated agent memory tools on Postgres + pgvector, enforced by row-level security.
Arachne MCP — scraping, browser, vision, OCR, RAG, Social Research, GitHub + VRT (56 tools).
Web scraping, browser RAG, vision, transcription, and 20 AI tools. Universal data intelligence.
Two-layer memory MCP server for AI agents with 37 tools, RAG, graphs, wiki, auth
x402 clean article text and metadata for AI/RAG. $0.002 per successful article.
On-device reranking that cuts RAG context tokens ~67% while keeping a relevant passage.
Astra DB tools for agents: schema, find, vector search, guarded writes, and interactive views.
Most memory solutions bolt on a vector store and call it done. BECOMER uses a proprietary retrieval engine benchmarked against LongMemEval, the standard academic benchmark for long-term…
Local RAG system with Playwright MCP integration for Claude
# Better Qdrant MCP Server A Model Context Protocol (MCP) server for enhanced Qdrant vector database functionality. This server provides tools for
A powerful MCP (Model Context Protocol) server for managing knowledge embeddings and vector search.
In this tutorial, you will learn how to build a full-featured Retrieval-Augmented Generation (RAG) server using IBM Watsonx.ai, ChromaDB for vector indexing, and expose it via the Model Context…
MCP server for Calypso (AIcore) agents via OpenAI-compatible API.
MCP tools for Malawian food search, clinical nutrition calculators, and RAG-backed guidance.
A Model Context Protocol (MCP) server with RAG (Retrieval-Augmented Generation) capabilities for intelligent document processing and question
Agent-first resource directory for AI agents: protocols, security, RAG, memory, evals, and more.
chatmem is a single-binary utility that captures your LLM conversations via Model Context Protocol tools any client can call (Claude Code, Cursor, aider, custom SDKs), stores everything in an…
A Model Context Protocol (MCP) server implementation that provides vector database capabilities through Chroma. This server enables semantic document search, metadata filtering, and document…
Embeddings, vector search, document storage, and full-text search with the open-source AI application database
MCP server for ChromaDB with fast batch file processing - ingest photos, CAD files, documents & code
Remote ChromaDB vector database MCP server with streamable HTTP transport
A Model Context Protocol (MCP) server integration for Chroma, the open-source embedding database.
This repository contains a curated list of public code packages that Chroma keeps indexed into Chroma collections. The repository currently indexes 3K+ packages across various registries. Please…
Inject, parse, and strip [N] citation markers in RAG outputs.
97-domain knowledge graphs for AI agents — 4× F1 of RAG, 11× fewer tokens. MCP-native.
Any URL to clean, LLM-ready Markdown for RAG. Strips ads, nav, and boilerplate.
Autonomous Repository Intelligence engine with web UI and MCP server. Unified semantic code understanding, local RAG, and agent working memory.
为指定的代码库目录创建语义索引。索引是实现 `SemanticCodeSearch` 的前提。此过程会在后台运行，并自动启用文件监控以进行增量更新。
A knowledge management tool for code repositories using vector embeddings. This tool helps maintain and query knowledge about your codebase using
An intelligent code memory system that leverages vector embeddings, structured databases, and knowledge graphs to store, retrieve, and analyze code
Build searchable AI research knowledge bases with hybrid vector search directly in Claude Desktop.
FAISS, call graph, AST, BM25 — 34 MCP tools for AI agents. 50-80% token reduction. Offline.
🌐 Website • 📊 Dashboard
A Model Context Protocol (MCP) server that enables AI consciousness persistence across sessions using RAG (Retrieval-Augmented Generation)
MCP memory server for AI coding assistants. Windows Service, ONNX embeddings, SQLite vector search.