"Technology pre-research resources" matching MCP servers:

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.

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.

将 pgvector 知识库检索能力封装为标准 MCP 服务器，支持 Tools、Resources 和 Prompts，使 Claude Desktop 等客户端可通过自然语言查询知识库。

A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.

Semantic search over Nordic economic data — market announcements, quarterly reports (162 companies), macro data (NO/SE/DK/FI), commodity prices, and press releases. 180,000+ vectors.

Enables Claude to search a local hybrid retrieval index of research papers and ingest new PDFs, providing research-paper memory queryable directly through natural language.

Enables AI agents to autonomously create and manage topic-specific vector knowledge bases with end-to-end functionality including project creation, content ingestion from URLs, semantic search, and progress tracking. Provides a complete research workflow without exposing low-level APIs.

Enables semantic search and contextual conversations with your Calibre ebook library using vector-based RAG technology. Supports project-based organization, multi-format book processing, and OCR capabilities for enhanced content extraction and retrieval.

An ultra-rational A2A protocol for zero-token edge pre-filtering and FEP-driven deadlock prevention. Uses Cloudflare Vectorize (384d cosine similarity) with a 24h deposit model, restricting bargaining to a 4-rally limit before forcing HTTP 402 dimension jumps.

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

Enables AI assistants to save, retrieve, and manage research content using ChromaDB vector storage with semantic search, topic organization, and automatic deduplication powered by OpenAI embeddings.

Enables MCP clients to remember user information, preferences, and behaviors across conversations using vector search technology. Built on Cloudflare infrastructure with persistent storage and semantic similarity matching.

Enables MCP clients to remember user preferences and behaviors across conversations using vector search technology.

MCP Memory is an MCP Server that gives MCP Clients the ability to remember information about users across conversations. It uses vector search technology to find relevant memories based on meaning, not just keywords.

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

An MCP server that gives AI assistants the ability to remember user information (preferences, behaviors) across conversations using vector search technology.