# Journal RAG

> Use this tool when you need to efficiently search and reference personal markdown journal entries using semantic similarity, solving the problem of manually searching through entries to recall past experiences and thoughts. It takes in markdown journal entries as input and outputs relevant entries based on semantic relevance, with options for full and incremental indexing. Ideal for use cases where users want to leverage their personal journal history in conversations with AI assistants, streamlining information retrieval and recall.

Canonical page: https://skillsregistry.net/skills/estevaom-journal-rag  
JSON: https://api.skillsregistry.net/v1/skills/estevaom-journal-rag

## Description

The Journal RAG MCP server enables AI assistants to search through personal markdown journal entries using semantic similarity. It implements a vector database approach with ChromaDB and sentence-transformers to index journal content, supporting both full and incremental indexing of entries. The server exposes two main tools: one for querying journal entries based on semantic relevance, and another for updating the index when new entries are added. Built with GPU acceleration support for faster embedding generation on NVIDIA hardware, this implementation is particularly useful for users who maintain personal journals in markdown format and want to reference past experiences, thoughts, and activities during conversations with AI assistants without manually searching through entries.

## Trust

- **Trust score (0–1):** 0.65
- **Verification tier:** scanned
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-09-02

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/estevaom-journal-rag)
- **Repository:** <https://github.com/estevaom/markdown-journal-rust>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "estevaom-journal-rag"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/estevaom-journal-rag` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/estevaom-journal-rag/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt