Claude Code Skills

notery-get

Fetch the full content of a document from your Notery workspace via MCP.

Overview

The notery_get tool retrieves the complete content of a single document by its ID. Use it after notery_search to fetch the full text of a relevant result.

This tool is available through both the MCP server and agent skills. It calls the underlying REST endpoint:

GET/api/v1/documents/:id

Fetch a single document by ID. See API Reference for direct HTTP usage.

Parameters

Parameter	Type	Required	Description
`id`	string	Yes	The document UUID. Obtain this from `notery_search` results.

Response

The tool returns formatted text containing the full document, including:

Title — the document title
Path — the full path within the workspace
File type — the document language or format (e.g. markdown, typescript, python)
Word count — total word count of the document content
Tags — any tags assigned to the document
Content — the complete document body

Example

Ask your AI assistant:

"Get the full content of document 550e8400-e29b-41d4-a716-446655440000"

The tool will return something like:

Title: Auth Architecture
Path: /backend/auth-architecture
Type: markdown
Words: 847
Tags: auth, backend, security

---

# Auth Architecture

## Overview

Notery uses BetterAuth with the Drizzle adapter for authentication.
Email/password is the primary auth method...

[full document content continues]

Typical Workflow

The notery_get tool is most useful as the second step in a search-then-retrieve pattern:

Search — use notery_search to find documents matching your question. Review the summaries and scores to pick the best match.
Retrieve — use notery_get with the document ID to fetch the full content. Your AI assistant can then reference the complete text when answering your question.

This keeps responses fast. The search step returns lightweight summaries, and full content is only fetched for the documents you actually need.

You can call notery_get multiple times in a single conversation to pull in content from several documents. This is useful when your question spans multiple topics that are covered in separate docs.