Chronicle MCP server

Chronicle (Universal Chat Connector)

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Chronicle is a production-grade Model Context Protocol (MCP) server designed to sync, clean, format, and index local artificial intelligence chat transcripts. By bridging the gap between local editor history and large language model contexts, Chronicle allows agents to search, compare, retrieve, and reference past conversation logs. It features optimized token-saving heuristics that compress code blocks and limit message lengths, reducing context window utilization by up to 40 percent.

Key Architectural Capabilities

Format Normalization Engine

AI providers and editor clients save conversation logs in diverse formats. Chronicle normalizes these structures into a standard role-and-content message format:

• ChatGPT Exports: ChatGPT exports conversation histories in recursive mapping node structures. Chronicle traverses and flattens these maps, sorts messages chronologically by creation timestamp, and extracts the plain-text message threads.
• Claude Exports: Claude structures messages as flat lists nested under the chat_messages field. Chronicle parses these lists, maps custom sender roles (such as human and assistant) to standard roles, and cleans the text strings.
• Generic and Markdown Formats: Chronicle includes parsers for flat JSON message lists (such as Cline or Continue) and structured Markdown logs (mapping headers like User and Assistant to message boundaries).

Context Token Optimization

Large chat logs can quickly exhaust context windows and increase API costs. Chronicle implements proactive token-saving mechanisms:

• Code Block Summarization: Automatically replaces verbose code blocks with metadata summaries indicating the programming language and line count. This behavior can be disabled on demand to read full code snippets.
• Length Limiting: Truncates extremely long individual messages at a configurable character threshold, appending a notice that the user can re-run the tool with expanded limits if necessary.

Context Window Token Savings Graph

To verify these savings, we executed multi-turn conversation benchmarks. The graph below displays total token usage over a sequence of message turns:

Shortened. The full README is on GitHub.