"A resource for conducting deep research" matching MCP servers:

Provides semantic search and complete document retrieval by ID through OpenAI's Vector Store API, letting deep research agents access company-specific knowledge and tools.

An MCP server that enables saving, retrieving, and managing research content using ChromaDB vector storage and semantic search.

Enables AI to save, organize, search, and synthesize research materials using a local vector database with support for both OpenAI and Ollama backends.

Enables semantic search and conversational querying across a personal research library of PDFs, DOCX, and other documents using a vector database. It provides tools for document summarization, finding related papers, and high-accuracy retrieval for AI clients like Claude Desktop.

Enables read-only collection research through six snapshot-backed tools for capabilities, facets, typed selection, ranking, bounded object metadata hydration, and evidence resolution.

Enables read-only semantic search and stats over locally ingested arXiv/OpenAlex paper metadata stored in ChromaDB and SQLite, without allowing writes through the retrieval interface.

Exposes search_knowledge and create_quote over Streamable HTTP with JWT validation, enabling permission-scoped knowledge retrieval and deterministic simulated quote creation while keeping pricing and approvals outside model control.

Enables passage-level semantic search over a Zotero library by extracting, chunking, and embedding PDF text using Gemini and ChromaDB. It provides MCP tools to perform topical searches and retrieve specific document passages with surrounding context.

A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.

An integration server implementing the Model Context Protocol that enables LLM applications to interact with Milvus vector database functionality, allowing vector search, collection management, and data operations through natural language.

Enables users to ingest PDF/DOCX/TXT/MD documents and ask natural language questions about them, using local embeddings and Groq-powered retrieval-augmented generation.

Munin is a high-performance, pragmatic memory layer for AI agents (Cursor, Claude Code, OpenClaw, Gemini CLI,...). Unlike other solutions, Munin focuses on developer productivity with: * Multi-Project Support: Isolate memories into separate "brains" (Context Cores). * GraphRAG: Automatically builds a knowledge graph from your context. * Sub-200ms Search: Blazing fast Hybrid & Semantic

Enables AI-powered document analysis and querying for project documentation using vector embeddings stored in Redis. Supports document upload, context-aware Q\&A, automatic test case generation, and requirements traceability through OpenAI integration.

A local Retrieval-Augmented Generation system that enables users to ingest markdown files into a FAISS-powered vector knowledge base for semantic search. It provides tools for document indexing and context retrieval to support informed LLM queries without external dependencies.

Vectorize MCP server for advanced retrieval, Private Deep Research, Anything-to-Markdown file extraction and text chunking.

Enables vector search and document retrieval, document-to-Markdown extraction and chunking, and private deep research through configured pipelines.

Enables vector search and document retrieval, text extraction and chunking into Markdown, and private deep research workflows through configured pipelines.

Enables advanced vector retrieval, document text extraction and chunking into Markdown, and private deep research through a configured pipeline.

Enables vector search and document retrieval, text extraction and chunking into Markdown, and private deep research through configured pipelines.

Enables vector search and document retrieval, extraction and chunking of files into Markdown, and private deep research through configured pipelines.

Enables vector search and document retrieval, text extraction and chunking into Markdown, and private deep research through Vectorize pipelines.

Enables users to perform vector search and document retrieval, extract and chunk files into Markdown, and generate private deep research reports from a configured pipeline.

Enables vector search and document retrieval, extraction and chunking of files into Markdown, and private deep research through a configured pipeline.

Enables vector search and document retrieval, text extraction and chunking into Markdown, and private deep research through a configured Vectorize pipeline.

Enables users to ingest documents into a PostgreSQL/pgvector knowledge base, run semantic search over them, and get grounded answers through a retrieval-augmented generation pipeline backed by a free LLM. It also lets clients spin up specialized AI agents on demand and exposes knowledge-base stats and configuration as resources for tutoring workflows.

A local-first Graph-RAG system combining ChromaDB with metadata-based graph relationships and Gemini 2.5 Flash for intelligent Q&A over Obsidian vaults, supporting MCP clients like Claude Desktop, Cursor, and Raycast.

A server implementation that allows secure communication between MCP clients and privateGPT, enabling users to chat with privateGPT using knowledge bases and manage sources, groups, and users through a standardized Model Context Protocol.

Enables vector search and document retrieval, text extraction and chunking into Markdown, and private deep research generation using configured pipelines.

Enables vector search and document retrieval, text extraction and chunking into Markdown, and private deep research through configured pipelines.

Enables vector retrieval over documents, extraction and chunking of files into Markdown, and private deep research through configured pipelines.