Unabyss: Universal Context Layer for AI Tools via MCP

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Unabyss synchronises work context from platforms like Slack, GitHub, Gmail, and Notion, delivering it across multiple AI tools via the Model Context Protocol (MCP). It eliminates the need to repeatedly re-explain context in ChatGPT, Claude, Cursor, and other tools.

Key points

  1. Centralised context layer: Aggregates and organises data from over 25 everyday applications, including Slack, Gmail, Notion, Linear, and GitHub.
  2. MCP integration: Serves structured context directly to AI models and code editors, such as Claude, Cursor, ChatGPT, Codex, and Gemini.
  3. Real-time synchronisation: Keeps context current, bypassing the staleness associated with static manual configuration files like CLAUDE.md.
  4. Data security and privacy: Features AES-256 encryption at rest, TLS 1.3 in transit, EU hosting for European users, GDPR compliance, and a strict no-training policy.
  5. Pricing structure: 7-day free trial; plans include Pro ($13/month, billed annually), Max ($79/month), and Team ($39/seat/month).

Why it matters

Manually copy-pasting project background, role definitions, and business context across different AI interfaces wastes significant time. A vendor-neutral context management layer backed by MCP prevents platform lock-in and keeps varied AI agents operating from a unified baseline.

Analysis

Unabyss directly tackles the siloed nature of proprietary features such as Claude Memory or ChatGPT Memory, which remain restricted to their respective ecosystems. By leveraging MCP, Unabyss functions as neutral middleware connecting data silos to LLMs. Benchmarks published by Unabyss with Claude Code show 38% faster task completion and 36% lower cost, illustrating the operational efficiency gained by automating structured context retrieval.

Mentioned links

Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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