Unabyss for Claude

Product Introduction

1. Definition: Unabyss is a universal context layer and MCP (Model Context Protocol) server that acts as a centralized, structured memory system for AI agents. It is a technical middleware solution that ingests, processes, and serves user context from various applications to multiple AI tools.
2. Core Value Proposition: It exists to eliminate AI context silos and the need for manual re-explanation. Its primary value is providing a portable, private, and live memory that follows users across different AI interfaces like Claude Desktop, Cursor, and ChatGPT, syncing context from integrated work apps automatically.

Main Features

1. Multi-Source Context Ingestion: Unabyss connects to over 25 productivity and development tools—including Gmail, Slack, Notion, GitHub, Google Drive, and meeting recorders (Fathom, tl;dv)—via OAuth. It performs automated data extraction, structuring unstructured information into a unified, queryable knowledge graph tagged by topic, sensitivity, and source.
2. MCP (Model Context Protocol) Server Integration: This is the core technical mechanism. Unabyss functions as a custom MCP server (https://mcp.unabyss.com). Users connect their AI clients (Claude Desktop, Cursor) to this endpoint once. Subsequently, every AI session can dynamically pull relevant, live context from the Unabyss layer on-demand, without manual file uploads.
3. Structured & Live Context Synchronization: Unlike static files, Unabyss maintains a continuously updated context. It structures extracted data (e.g., "Q3 launch ships Oct 24" tagged with topic: launch, confidential, source: Slack) and ensures changes in connected sources are reflected. This synchronized context is then made available to all connected AI agents, ensuring consistency.

Problems Solved

1. Pain Point: AI Context Fragmentation and Amnesia. Users waste time re-explaining their role, projects, and historical decisions every time they switch between AI tools (Claude, ChatGPT, Cursor) or start a new chat. Critical context remains trapped in individual chat histories or external apps invisible to AI.
2. Target Audience: Technical Professionals and Teams who use multiple AI agents daily. Key personas include: Founders (managing company strategy), Builders/Developers (coding across repos), Agency Operators (managing multiple client contexts), and GTM (Go-To-Market) Specialists (handling pipeline and outreach).
3. Use Cases: A developer using Cursor for coding can have Claude automatically understand recent PRD discussions from Notion and API decisions from Slack. A founder can switch from drafting an investor update in ChatGPT to analyzing pipeline in Claude without re-summarizing the company status. An agency can keep each client's context separated and accurately fed into AI for client-specific work.

Unique Advantages

1. Differentiation vs. Native AI Memory & Manual Files: Unlike ChatGPT Memory or Claude Memory (siloed within each vendor's ecosystem) or a static CLAUDE.md file (manually maintained, quickly stale), Unabyss is vendor-agnostic, automated, and live. It provides a single source of truth that updates automatically and is accessible via open protocol (MCP) to any compatible tool.
2. Key Innovation: The product's core innovation is its implementation as a structured MCP server over a unified context graph. This technical approach allows it to act as a neutral, user-owned layer between data sources and AI consumers. The system's ability to tag, segment, and serve only relevant slices of context on-demand, while maintaining live connections to source apps, is its defining technical advantage.

Frequently Asked Questions (FAQ)

1. How does Unabyss for Claude work with MCP? Unabyss operates as a custom MCP (Model Context Protocol) server. After signing up, you add https://mcp.unabyss.com as a custom connector in Claude Desktop. Once authorized, Claude can query Unabyss in real-time to retrieve your structured, live context from connected apps like Gmail and Notion during conversations.
2. Is Unabyss better than Claude's own memory feature? Yes, for cross-tool context. Claude Memory only works within Claude chats. Unabyss creates a centralized memory layer that works with Claude, Cursor, ChatGPT, and other MCP-compatible tools, pulling from your actual work apps and syncing what you learn in one AI to all others.
3. What is the difference between Unabyss and a context file? A context file (e.g., CLAUDE.md) is a static, manual snapshot you must constantly update and copy-paste. Unabyss is a dynamic, automated system. It stays in sync with your connected data sources (Slack, GitHub, etc.) and also learns from your AI interactions, creating a living context that never goes stale.
4. Is my data safe with Unabyss? According to its documentation, Unabyss employs enterprise-grade security: data is encrypted with AES-256 at rest and TLS 1.3 in transit. It uses OAuth scoped permissions, allows instant access revocation, offers EU-hosted data for GDPR compliance, and states it does not sell data or use it for AI model training.
5. Can Unabyss connect to both Claude and Cursor? Absolutely. This is a primary use case. By connecting Unabyss as an MCP server to both Claude Desktop and the Cursor IDE, both tools will have access to the same synchronized, structured context from your linked apps, enabling seamless workflow between conversational AI and your coding environment.