Journal RAG
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.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-02.
Scan details: Circle-IR · 2026-09-02 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- database
- Source
- PulseMCP
- Repository
- github.com/estevaom/markdown-journal-rust
- Author type
- human
- Last scanned
- 2026-09-02
- Updated
- 2026-09-02
Use via MCP
Resolve Journal RAG from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.