"Qase" matching MCP servers:

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

基于MCP协议的知识库服务器，内置LLM层，提供结构化知识存储、语义搜索和主动知识推送，支持通过工具进行知识学习、搜索和关联推理。

Provides semantic search and data retrieval capabilities over a knowledge base with multiple tools including keyword search, category filtering, and ID-based lookup with in-memory caching.

A local document processing server that can index various document formats (PDF, DOCX, TXT, HTML) and answer questions based on their content using the Model Context Protocol.

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 capture, classify, synthesize, and retrieve persistent shared knowledge from sources such as Obsidian, code, transcripts, bookmarks, and sessions. It provides token-efficient search, context briefings, and tiered memory through MCP and REST APIs.

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.

Enables AI agents and clients to capture, classify, synthesize, and retrieve persistent knowledge from multiple sources through MCP and REST APIs, with tiered memory and token-optimized context.

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.

A lightweight personal knowledge base MCP server that lets clients store, search, organize, and summarize research notes from a local JSON file.

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 persistent memory by ingesting sources, classifying and synthesizing knowledge, and retrieving token-optimized context through MCP and REST APIs.

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.

Enables parsing multi-turn Slack and WeChat Work troubleshooting threads to extract validated Q&A resolution pairs, deduplicate organizational knowledge, and synthesize structured Markdown SOP runbooks via MCP.

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.

Provides tools for listing and retrieving content from different knowledge bases using semantic search capabilities.

A local knowledge base server that connects to AI assistants, turning markdown files into a semantically searchable memory layer via OpenAI embeddings and SQLite.