"A server to build a knowledge base of Node.js packages with RAG search functionality" matching MCP servers:

A knowledge base MCP server that aggregates team knowledge from multiple sources into Postgres. It provides hybrid search (full-text + vector + RRF) via MCP tools, and enables direct recording of decisions, learnings, and pitfalls.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Enables users to build and manage a complete retrieval-augmented generation pipeline through conversation, including file ingestion, collection management, hybrid search, reranking, citations, and a guided setup wizard.

Allows MCP clients to create, validate, preview, and submit product showcases on Build with AK, with support for local stdio and remote HTTP transports.

Exposes a knowledge base to LLM clients through tools, resources, and prompts.

Enables AI agents to share a persistent, self-improving memory by ingesting, classifying, synthesizing, and retrieving knowledge from sources like Obsidian vaults, code, and transcripts through MCP and REST APIs. Provides token-optimized, tiered retrieval with full-text and semantic search so every agent session builds on accumulated context.

Enables AI agents to share a persistent, self-learning memory by ingesting and classifying knowledge from Obsidian, code, transcripts, bookmarks, and terminal sessions, then retrieving token-optimized context through MCP and REST APIs.

A cross-project knowledge base MCP server supporting three-layer search (text matching, TF-IDF, and semantic vectors) with a Web UI for management.

Enables AI agents to share persistent memory by capturing, classifying, synthesizing, and retrieving knowledge through token-optimized, three-tier retrieval with hybrid keyword and semantic search. Supports bidirectional Obsidian sync, session and bug-fix capture, and a self-learning loop that compounds context across sessions.

Gives AI agents a shared persistent memory by ingesting Obsidian vaults, code, transcripts, web clips, and session or bug-fix captures, then serving token-optimized context briefings, keyword and semantic search, and cross-source synthesis. Enables multiple agents to read and write the same growing knowledge base so each session builds on the last.

Enables AI agents to share persistent memory by ingesting, classifying, synthesizing, and retrieving knowledge through MCP and REST APIs, with token-optimized, tier-aware search and Obsidian sync.

Enables AI agents to share a persistent, self-improving memory by ingesting sources like Obsidian vaults, code, transcripts, and session captures, then serving them back through search, classification, synthesis, and token-optimized context briefings. It lets multiple agents read and write the same knowledge base, so each session builds on the last without copy-pasting context.

Gives AI agents a shared persistent memory by ingesting notes, code, transcripts, bookmarks, and session captures into a SQLite FTS5 knowledge base they can search, read, classify, promote, and synthesize over MCP. This lets multiple agents retrieve tiered, token-optimized context and compound what they learn across sessions instead of starting from zero.

Enables AI agents to share persistent, self-improving memory by ingesting, classifying, synthesizing, and retrieving knowledge from Obsidian vaults, code, documents, transcripts, bookmarks, and sessions through MCP and REST interfaces with token-optimized search.

Enables AI agents to capture, classify, synthesize, and retrieve shared persistent knowledge across sessions via MCP, with tiered memory, full-text and semantic search, and token-efficient context briefings.

A persistent memory system that captures, classifies, synthesizes, and retrieves knowledge for AI agents, turning raw information into refined, retrievable knowledge that compounds intelligence over time.

Enables AI agents to share a persistent, self-improving memory by ingesting sources such as Obsidian vaults, code, transcripts, and bookmarks, then classifying, promoting, synthesizing, and retrieving that knowledge through token-optimized search across MCP and REST interfaces. Agents can capture sessions and fixes so context compounds across sessions and across Claude, Codex, Gemini, and any other MCP client.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

Enables AI agents to capture, classify, synthesize, and retrieve persistent knowledge through MCP and REST APIs, with SQLite FTS5 search, tiered memory, and token-optimized context so multiple agents share and compound context across sessions.

Enables AI agents to capture, classify, promote, synthesize, and retrieve persistent knowledge from sources like Obsidian vaults, code, transcripts, and session logs, serving it through MCP tools and a REST API. Provides tiered, token-optimized memory so multiple agents share one compounding brain instead of starting from zero each session.