# TriliumNext Notes

> Use this tool when you need to manage notes and knowledge bases through conversational AI interfaces. It solves problems of context switching and enables users to create, retrieve, update, search, and delete notes using natural language inputs, with outputs in various note formats such as text, code, and image. It is ideal for use cases where seamless integration of note management with AI assistants is required.

Canonical page: https://skillsregistry.net/skills/tan-yong-sheng-triliumnext-notes  
JSON: https://api.skillsregistry.net/v1/skills/tan-yong-sheng-triliumnext-notes

## Description

TriliumNext-MCP is a server that connects AI assistants to TriliumNext Notes, enabling note management through conversation. It provides tools for creating, retrieving, updating, searching, and deleting notes via the TriliumNext API. The implementation uses TypeScript and the Model Context Protocol SDK to expose a set of tools that handle various note operations, including content retrieval, note creation with different types (text, code, image), and searching with customizable parameters. This server is particularly valuable for users who want to manage their knowledge base directly through AI assistants without switching contexts.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** media
- **Updated:** 2026-04-25

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/tan-yong-sheng-triliumnext-notes)
- **Repository:** <https://github.com/tan-yong-sheng/triliumnext-mcp>

## 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": "tan-yong-sheng-triliumnext-notes"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/tan-yong-sheng-triliumnext-notes` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/tan-yong-sheng-triliumnext-notes/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