kaas MCP Server
bybit-exchange/kaas
[](https://agentrank-ai.com/tool/bybit-exchange--kaas/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge) Turn scattered notes, docs and transcripts into a queryable Markdown wiki — an LLM knowledge-base compiler with MCP access, no embeddings, self-hosted.
claude mcp add agentrank -- npx -y agentrank-mcp-server Overview
bybit-exchange/kaas is a Python tool licensed under MIT. Turn scattered notes, docs and transcripts into a queryable Markdown wiki — an LLM knowledge-base compiler with MCP access, no embeddings, self-hosted. Topics: ai-agents, claude-code, golang, knowledge-base, knowledge-management, llm, markdown, mcp, model-context-protocol, openai-api, personal-knowledge-management, python, rag, react, second-brain, self-hosted, sqlite, typescript, wiki.
Ranked #4583 out of 24846 indexed tools.
Ecosystem
Score Breakdown
149 stars → growing community
Last commit 2mo ago → moderate activity
9/15 issues closed → decent issue management
3 contributors → small team
No dependents → no downstream usage
Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →
How to Improve
Badge all embed codes →
[](https://agentrank-ai.com/tool/bybit-exchange--kaas/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge) <a href="https://agentrank-ai.com/tool/bybit-exchange--kaas/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge"><img src="https://agentrank-ai.com/api/v1/badge/bybit-exchange--kaas" alt="AgentRank"></a> Matched Queries
From the README
# KaaS — Knowledge as a Service **English** · [中文](README.zh-CN.md) Turn scattered notes, documents, and transcripts into a searchable, queryable personal Wiki — powered by LLM-driven knowledge compilation. **[Documentation](https://bybit-exchange.github.io/kaas-doc/)** · [Quick Start](#quick-start) · [MCP access](#mcp-access) ## What Makes This Different Unlike typical RAG systems that chunk and embed raw text, KaaS **compiles** your content through a 4-phase LLM pipeline. What comes out is human-readable Markdown articles — not a black-box vector store. ## The Compiled Wiki What you get is a tree of Markdown articles: categories on the left, the rendered article in the middle, its headings down the right. Chat answers cite these articles by name, so an answer and the page it came from are one click apart. ## How It Works Raw content passes through four LLM phases: extract concepts, entities and decisions; classify them into articles; write or merge the Markdown; update the iRead full README on GitHub →
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