Private Journal Mcp

Private Journal MCP Server

The Private Journal MCP server bridges the gap between an AI assistant and a developer’s personal knowledge base by providing a fully local, structured journaling system with powerful semantic search. It lets Claude capture fleeting thoughts, technical insights, and contextual notes in a single place while keeping all data on the developer’s machine—no external API calls or cloud storage are required. This privacy‑first design is crucial for teams working on proprietary codebases, sensitive research, or personal projects where data leakage is unacceptable.

At its core, the server exposes a set of intuitive tools that mirror common journaling workflows. The tool writes entries into distinct categories—feelings, project notes, user context, technical insights, and world knowledge—each stored in Markdown files with YAML frontmatter. Entries are automatically timestamped to microsecond precision, enabling precise chronological sorting and retrieval. The server’s dual‑storage model keeps project‑specific notes alongside the code repository while personal reflections live in a user‑home directory, ensuring that related information stays close to the relevant context.

Search and discovery are where the server truly shines. Leveraging local AI embeddings via @xenova/transformers, performs semantic queries that surface conceptually related entries rather than relying on brittle keyword matching. The tool supports filtering by scope (project, user, or both), sections, and result limits, making it easy to drill down into the most relevant insights. Complementary tools such as and provide quick access to full content or recent activity, streamlining the review process during code reviews or brainstorming sessions.

Integrating this MCP into an AI workflow is straightforward: developers add the server to Claude’s configuration, then invoke the tools through natural language prompts. For example, a developer might ask Claude to “summarize my feelings about the last sprint” or “search for all entries mentioning ‘vector similarity’ in the project journal.” Claude can then retrieve, synthesize, and present the information directly within its conversation, dramatically reducing context‑switching and keeping knowledge in the loop.

The standout advantage of Private Journal is its combination of privacy, performance, and semantic intelligence. By running entirely offline with in‑memory similarity calculations, the server delivers near‑instant responses even as the journal grows. Its robust fallback mechanisms ensure reliable file resolution across platforms, making it a dependable companion for any developer who values introspection, continuous learning, and secure knowledge management.