About Arezzo
Arezzo is a deterministic compiler for Google Docs API operations, aimed at developers building agents that edit documents. It converts semantic edit instructions into correctly indexed batchUpdate requests, handling UTF-16 offsets and cascading shifts.
Key features
- 01Compile semantic operations into batchUpdate requests
- 02Address content by heading, named range, or bookmark
- 03Calculate UTF-16 indices
- 04Track offsets across multiple edits
- 05Support text, table, and formatting operations
Use cases
- 01Insert or replace content by heading
- 02Avoid index errors in batched edits
- 03Edit tables or text styles through an agent
- 04Read document structure before targeted changes
MCP support
Arezzo exposes tools for reading document structure and editing documents through MCP, with the server making Google Docs API calls. The editing tool compiles semantic operations into batchUpdate requests.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
uvx arezzoGOOGLE_APPLICATION_CREDENTIALSSecretPath to the Google OAuth2 client credentials JSON (Desktop application type, Docs API enabled). Required — the Google Docs API does not support anonymous access. Run 'arezzo init' to walk through OAuth setup and write platform config.Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEArezzo
Deterministic compiler for Google Docs API operations.
You cannot safely modify a Google Doc by constructing batchUpdate requests yourself. The API uses UTF-16 code units with cascading index shifts — insert 10 characters at position 50, and every subsequent index in your batch is now wrong. A single miscalculation silently corrupts the document with no error message.
Arezzo compiles semantic intent into a correct request sequence. Tell it what you want to do; it handles the index arithmetic.
For AI agents (MCP tools)
Arezzo exposes three tools via the Model Context Protocol:
read_document(document_id)
→ Returns the document's structural map: headings with hierarchy,
named ranges, tables, section boundaries. Call this before editing
so you know what addresses are available.
edit_document(document_id, operations)
→ Compiles operations into correct batchUpdate requests and executes
them. Handles UTF-16 arithmetic, cascading index shifts, and
OT-compatible request ordering. Supported operations: insert/delete/
replace text, formatting (bold, italic, headings, links), tables,
lists, images, headers/footers, footnotes, named ranges.
validate_operations(document_id, operations)
→ Compile-only dry run. Returns the compiled requests for inspection
without executing. Use before edit_document when uncertain.
Operation format
{
"type": "insert_text",
"address": {"heading": "Revenue Analysis"},
"params": {"text": "New paragraph content.\n"}
}
Address modes:
{"heading": "Section Name"}— by heading text{"named_range": "range_name"}— by named range{"bookmark": "bookmark_id"}— by bookmark ID{"start": true}— document start{"end": true}— document end{"index": 42}— absolute UTF-16 index
Operation types:
insert_text, delete_content, replace_all_text, replace_section,
update_text_style, update_paragraph_style, insert_bullet_list,
insert_table, insert_table_row, insert_table_column,
delete_table_row, delete_table_column, insert_image,
create_header, create_footer, create_footnote,
create_named_range, replace_named_range_content, insert_page_break
Recommended workflow
read_document → edit_document → (if structural changes) read_document → edit_document
Always read before editing. After inserting structural elements (tables, headers, footers), read again to get the new element indices before adding content inside them.
Installation
pip install arezzo
arezzo init
arezzo init walks through Google OAuth setup and writes platform config files for your MCP client.
Setup
Prerequisites: A Google Cloud project with the Google Docs API enabled and an OAuth 2.0 client ID (Desktop application type).
arezzo init
The wizard:
- Copies your
credentials.jsonto~/.config/arezzo/ - Runs the OAuth consent flow (browser opens once)
- Generates config files for Claude Code, Cursor, and VS Code
For Claude Desktop, arezzo init prints the config block to add manually.
Platform configs
After arezzo init, config files are written to your project directory:
Claude Code / Cursor (.mcp.json):
{
"mcpServers": {
"arezzo": {
"command": "arezzo"
}
}
}
VS Code (.vscode/mcp.json):
{
"servers": {
"arezzo": {
"type": "stdio",
"command": "arezzo"
}
}
}
Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"arezzo": {
"command": "arezzo"
}
}
}
Why Arezzo exists
The Google Docs batchUpdate API operates on UTF-16 code units with absolute index positions. Every character insertion or deletion shifts all subsequent indices. In a batch with multiple mutations, each request's indices must account for the effect of every prior request in the same batch.
Getting this right requires:
- UTF-16 length calculation (not Python
len()— surrogate pairs count differently) - Reverse-order execution for same-type mutations (delete from end to start)
- Two-phase compilation (content mutations before format mutations)
- Cascading offset tracking across multi-step operations
Arezzo handles this deterministically. The same input always produces the same output. No reasoning, no guessing, no "usually works."
Architecture
semantic operation
↓
arezzo.parser.parse_document() — build heading/range/bookmark indexes
↓
arezzo.address.resolve_address() — semantic reference → document index
↓
arezzo.operations.* — operation → batchUpdate request(s)
↓
arezzo.index.sort_requests() — OT-compatible mutation ordering
↓
correct batchUpdate request sequence
The engine is a pure function: compile_operations(doc, operations) → requests. Deterministic. No side effects. No API calls.
The MCP server (arezzo.server) wraps the engine with Google Docs API I/O and behavioral guidance fields (next_step, present_to_user, document_reality).
License
MIT — Convergent Methods, LLC
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.