gdoc-comments
An MCP server for reading/editing Google Docs content and managing comments, including listing, creating, replying, resolving, and reopening comments via the Docs API and Drive API.
README
gdoc-comments MCP server
An MCP server for reading/editing Google Docs content and managing comments (list, create, reply, resolve, reopen), backed by the Docs API and the Drive API's comments endpoints.
Why a custom server instead of an existing one
Claude Code already ships with a first-party Google Drive connector
(mcp__claude_ai_Google_Drive__*), and there's no shortage of third-party
"Google Docs MCP" projects on GitHub/npm. In practice neither covers actual
comment management:
- The built-in Drive connector exposes 8 tools total —
search_files,list_recent_files,get_file_metadata,get_file_permissions,read_file_content,download_file_content,create_file,copy_file. Onlyread_file_contenttouches comments at all, via anincludeCommentsflag that inlines them as read-only text tags in the document body. There's no comment listing with structured IDs or resolved status, and no way to create a comment, reply to one, or resolve/reopen a thread. - Third-party servers vary widely in whether they touch comments at all, and where they do, comments created through the Drive API generally aren't anchored to a text selection in the Docs UI (a Drive API limitation this server inherits too — see the note under "Tools exposed" below).
This server exists specifically to close that gap: full read/write comment lifecycle management (list, create, reply, resolve, reopen), not just read-only visibility into comments that already exist.
1. Google Cloud setup (one-time)
- Create or pick a project at https://console.cloud.google.com/.
- Enable two APIs for that project:
- Google Docs API
- Google Drive API
- Configure the OAuth consent screen (External is fine; keep it in "Testing" mode) and add your own Google account under Test users.
- Create an OAuth client ID: APIs & Services → Credentials → Create Credentials → OAuth client ID → Application type: Desktop app.
- Download the client JSON and save it as
credentials.jsonin this directory (already gitignored).
2. Install
python3 -m venv .venv
.venv/bin/pip install -r requirements.txt
3. Authenticate (one-time, interactive)
.venv/bin/python authenticate.py
This opens a browser for Google login/consent and caches a token to
token.json (gitignored). Since the OAuth consent screen stays in "Testing"
mode, click through the "unverified app" warning — that's expected for an
app only you use. The MCP server itself never triggers this flow; it only
reads and silently refreshes this cached token. Re-run this script if the
token is ever revoked or deleted.
4. Wire it into Claude Code
The server's command always points at wherever this repo was cloned — the
venv and server.py live here, not in whatever project you happen to be
registering the tool for. Set that path once:
GDOC_MCP_DIR=/absolute/path/to/gdoc_comment_mcp # wherever you cloned this repo
PYTHONPATH must point at $GDOC_MCP_DIR — server.py is launched directly
(not via -m), so without it Python can't find the gdoc_mcp package it's
part of, and the server crashes immediately on every connection attempt.
Local scope (private to you, this project only)
The default for claude mcp add. Run from anywhere, including a different
project's directory — only $GDOC_MCP_DIR needs to be right, not your pwd:
claude mcp add gdoc-comments -e PYTHONPATH="$GDOC_MCP_DIR" -- "$GDOC_MCP_DIR/.venv/bin/python" "$GDOC_MCP_DIR/gdoc_mcp/server.py"
User scope (private to you, available in every project)
Add -s user so the server shows up regardless of which directory you launch
claude from — no need to re-register it per project:
claude mcp add gdoc-comments -s user -e PYTHONPATH="$GDOC_MCP_DIR" -- "$GDOC_MCP_DIR/.venv/bin/python" "$GDOC_MCP_DIR/gdoc_mcp/server.py"
Project scope (shared with anyone who clones a given project)
Add -s project while standing inside that other project's directory —
this writes a .mcp.json there (not in gdoc_comment_mcp), which you'd
commit so teammates get the server automatically when they open that project
in Claude Code:
cd /path/to/some/other/project
claude mcp add gdoc-comments -s project -e PYTHONPATH="$GDOC_MCP_DIR" -- "$GDOC_MCP_DIR/.venv/bin/python" "$GDOC_MCP_DIR/gdoc_mcp/server.py"
Each teammate still needs to complete their own Google Cloud
setup and authenticate locally —
.mcp.json only wires up the server command, not credentials.
Tools exposed
read_google_doc(document_id)— title + flattened text contentinsert_text_in_doc(document_id, index, text)append_text_to_doc(document_id, text)replace_text_in_doc(document_id, find_text, replace_text, match_case)list_doc_comments(document_id, include_resolved)get_doc_comment(document_id, comment_id)create_doc_comment(document_id, content, quoted_text)reply_to_doc_comment(document_id, comment_id, content)resolve_doc_comment(document_id, comment_id, content)reopen_doc_comment(document_id, comment_id, content)
document_id accepts either a bare document ID or a full
https://docs.google.com/document/d/... URL.
Note: new comments created via the API are not anchored to a specific text
selection — the Drive API doesn't expose that. quoted_text is shown as
context alongside the comment but won't highlight a range in the doc.
Tests
.venv/bin/python -m pytest tests/
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.