Context Continuation MCP Server

Context Continuation is an MCP server that keeps your AI development sessions coherent even when token limits are reached. It tracks the flow of a conversation, records key decisions and milestones, and can generate a concise restoration prompt that lets you resume an interrupted session without losing context. By persisting information in plain‑text Markdown files, the server stays lightweight, version‑control friendly, and easy to inspect or edit manually.

The core problem it solves is the frequent loss of context that developers face when interacting with large language models. Every time a session reaches its token budget, the assistant forgets earlier messages and decisions. Context Continuation monitors token usage in real time and alerts you before the limit is hit, giving you a chance to save or summarize. When a new session starts, the server can automatically assemble the most relevant parts of past conversations into a single prompt that restores the assistant’s memory of the project.

Key capabilities include:

• Automatic context tracking – The server counts tokens and logs every message you flag as important.
• Intelligent session breaks – A warning is issued when the token budget approaches its ceiling, allowing you to pause or archive.
• Seamless restoration – A single command produces a concise prompt that re‑establishes the assistant’s understanding of your project.
• Project management – Milestones, decisions, and progress updates are stored in dedicated Markdown files that integrate with git workflows.
• Human‑readable storage – All data lives in a directory, making it easy to review or hand‑edit.

Real‑world use cases span from rapid prototyping to long‑term research projects. A front‑end developer can start a design discussion, let the server capture key UI decisions, and later resume with those decisions automatically in context. A data scientist can track model architecture choices across experiments, and a product manager can keep release milestones logged while the AI drafts documentation. Because the server exposes MCP tools, any client that understands MCP—Claude Desktop, custom scripts, or other AI assistants—can tap into these features without modifying the assistant’s core logic.

Integrating Context Continuation into an AI workflow is straightforward: initialize a session, flag critical messages with , and let the server handle token monitoring. When a session ends, produces a summary that can be reused. The restoration prompt produced by can be fed back into any MCP‑compliant assistant, ensuring continuity across disjointed interactions.

In summary, Context Continuation transforms the way developers interact with language models by preserving conversation state, automating context restoration, and maintaining a transparent record of project progress—all through simple MCP tools that fit naturally into existing development environments.