information-retrieval
7 servers · 49★ total
A Model Context Protocol (MCP) compliant server designed for comprehensive web research. It uses Tavily's Search and Crawl APIs to gather detailed information on a given topic, then structures this data in a format perfect for LLMs to create high-quality markdown documents.
High-performance, low-latency MCP server for local code context. Features AST-aware symbol graphs, semantic reranking, and PageRank-scored search for AI agents (Claude, Cursor, and more).
Rust-native universal retrieval layer for humans and AI agents — search, extract, rank, and synthesize the web. CLI + REST API + MCP server, with adaptive extraction (CEP), token-budgeted packing, multi-signal ranking, and cited research.
RE-call — Retrieval-Augmented Self-Recall: RAG over an AI agent's own memory that knows when it doesn't know (gap detection, freshness, anti-re-litigation). PostgreSQL + pgvector, hybrid retrieval + RRF.
Portable hybrid (BM25 + dense + RRF) retrieval engine and a label-free evaluation harness — extracted from a personal AI-assistant memory index and decoupled to run on any source tree.
Research intake that reaches the hard places: web, video, papers, scanned PDFs, browser, OCR, and audio into structured research packets. DOM extraction and change tracking built in; provenance rides along on every item.
MCP Tool, that can utilize multiple search engines and processing to provide AI ready results