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private-journal-mcp MCP Server

obra/private-journal-mcp

Score: 47.9 Rank #22439 MCP Server
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A lightweight MCP server that provides Claude with a private journaling capability to process feelings and thoughts

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Overview

obra/private-journal-mcp is a TypeScript MCP server licensed under MIT. A lightweight MCP server that provides Claude with a private journaling capability to process feelings and thoughts

Ranked #22439 out of 24846 indexed tools.

Ecosystem

TypeScript MIT

Score Breakdown

StarsFreshnessIssue HealthContributorsDependents
Stars 15% 454

454 stars → growing community

Freshness 25% 2mo ago

Last commit 2mo ago → moderate activity

Issue Health 25% 31%

5/16 issues closed → many open issues

Contributors 10% 3

3 contributors → small team

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A
PyPI Downloads N/A
Forks 91
Description Good
License MIT

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

Description low impact

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Freshness high impact

Last commit was 79 days ago — a recent commit would boost your freshness score

Issue Health high impact

You have 11 open vs 5 closed issues — triaging stale issues improves health

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Matched Queries

"mcp server""mcp-server"

From the README

# Private Journal MCP Server

A comprehensive MCP (Model Context Protocol) server that provides Claude with private journaling and semantic search capabilities for processing thoughts, reflections, and insights.

## Features

### Journaling
- **Multi-section journaling**: Separate categories for reflections, observations, project notes, user context, technical insights, and world knowledge
- **Dual storage**: Project notes stay with projects, personal thoughts in user home directory
- **Timestamped entries**: Each entry automatically dated with microsecond precision
- **YAML frontmatter**: Structured metadata for each entry

### Search & Discovery
- **Semantic search**: Natural language queries using local AI embeddings
- **Vector similarity**: Find conceptually related entries, not just keyword matches
- **Local AI processing**: Uses @xenova/transformers - no external API calls required
- **Automatic indexing**: Embeddings generated for all entries on startup and ongoing

### Privacy 
Read full README on GitHub →

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