"Information about PG Vector" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
graphrag-ts is a TypeScript GraphRAG engine on PostgreSQL/pgvector, served as a Streamable HTTP MCP endpoint. Public read tier needs no credentials (20 req/min per IP): integrity_paper_status, retraction_propagation, integrity_evidence_audit. A bearer key unlocks graph query, global map-reduce, index jobs and memory writes. Free browser demo: https://huggingface.co/spaces/ashesborn6/retraction-status
Cloud or self-hosted knowledge for AI agents: hybrid search, reranking, GraphRAG, scoped MCP tools.
The only News based AI MCP your agents will ever need — custom categories, global regions, and time-scoped results in one tool. We use multi-vector & sparse-hybrid search to search through thousands of articles across the world to find the exact news you're looking for.
Serverless hybrid search: full-text, vector and faceted search, in SQL, ES DSL, Solr and more.
Ask questions about Claude Code, Cursor, Codex and MCP — get answers grounded in their official docs, with citations. A hybrid RAG service that AI agents call through two MCP tools (or over REST). It indexes ~600 pages of official documentation, refreshes itself daily, and every design choice is backed by an experiment.
Answers questions about Radu Stoian, Technical Director at Enhance Media.
Historical market memory for AI agents using semantic vector search across years of financial market data. Discover similar market regimes, price patterns, and market context for quantitative research and algorithmic trading.
Answers about vareen.net from its website: products, technical data, comparisons, sales contact.
Compress, merge, split, convert, OCR, redact, sign and create PDFs, and ask questions about them, without leaving the chat. Sign in with a FynePDF account (OAuth) or use an API key; results come back as files you can download or keep.
Facts about Rankera, the done-for-you AI visibility service: plans, channels and site search.
Any URL to clean text, markdown, or JSON for LLM/RAG pipelines.
Long-term memory for AI agents: learns what is true about each person and answers with sources.
The Brains turns founders, CEOs and investors (Paul Graham, Naval Ravikant, Pieter Levels, Alex Hormozi, Sam Altman and 20+ more) into searchable knowledge bases built from their public talks, interviews, essays and posts. Ask how they think about growth, distribution, marketing, pricing and building a company, and get passages with a link to the original source. Search everyone at once or one person. Free and open source (MIT). Sign in with your email. Unofficial: not affiliated with or endorse
Memwyre is an MCP-native persistent memory layer for AI agents, synchronizing context across Claude Code, Cursor, VS Code, and OpenClaw. Built with a high-precision retrieval architecture (dense vector search, BM25, and cross-encoder reranking), Memwyre achieves a benchmarked 73.1% accuracy on the Long-Context Memory (LoCoMo) benchmark. It provides secure, isolated knowledge vaults with dedicated tools (search_memwyre, save_memory, list_memories) to save and recall structured project decisions.
Disposable private vector search + semantic RAG for AI agents. x402 pay-per-call, no account.
Remote ChromaDB vector database MCP server with streamable HTTP transport
Vector RAG store for Word/Excel/PDF/PowerPoint. Break-even pricing, $5 per 5,700 pages.
Answers about your own company from its recorded knowledge, and shows where its sources disagree.
Rafter holds a team's durable knowledge — skills, agents and memory files, each versioned — and serves it to AI tools over MCP. Agents search across the team's artifacts before answering questions about how the team works or what was decided, fetch full artifact text along with its citation edges (cites, cited_by, links) to explore related material, and write new learnings back as memory. Also covers workspace, team and membership management.
MCP memory server with shared team workspaces, typed knowledge chunks (decision, finding, convention, state, question, reference), role-based access, and cross-tool support for Claude, Cursor, and Codex. The only MCP memory server built for engineering teams. Features automatic deduplication, two-layer retrieval (LLM KB selection + hybrid vector/BM25/RRF fusion), a web dashboard with knowledge graph visualization, and attribution tracking. Zero server-side LLM costs.
ContextBook is an open-source MCP server that gives AI tools a persistent, searchable context library. Store information as Books and Pages, retrieve exactly what's needed via natural-language semantic search - injected on demand, not pre-loaded. Works with Cursor, Claude, Windsurf, and any MCP-compatible client. Self-hostable, MIT licensed.
Make your knowledge agent-ready. Connect docs from Confluence, Notion, GitHub, Dropbox, or Google Drive — any AI agent searches them via one MCP endpoint. 3 retrieval modes: vector search, broad search, and full document access. The agent decides how deep to dig.
A collaborative substrate over your data: vector, knowledge graph, SQL, geospatial, streaming.
Answers questions about a business using its indexed website content, with cited sources.
NeuralBrain MCP Server - RAG, Vector Memory, LLM Routing, Agent Identity, x402 Payments
The Needle MCP server enables semantic search on documents stored in files like PDFs, DOCX, and XLSX by connecting AI applications to external data sources. It provides capabilities to create and manage document collections, perform natural language searches on stored content, and retrieve relevant information without requiring exact keyword matches.
Persistent memory and knowledge graph for AI assistants — keyword + vector + graph search.
Provides metadata information to AI agents through the search API.
