gmail-mcp MCP Server
Provides an interface to the Gmail API for managing a text-based notes system, allowing for the creation, storage, and summarization of notes via MCP tools and resources.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@gmail-mcp MCP Server Create a note about the project kickoff meeting tomorrow at 10 AM"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
gmail-mcp MCP Server
a gmail mcp server built on top of typescript and the gmail api
This is a TypeScript-based MCP server that implements a simple notes system. It demonstrates core MCP concepts by providing:
Resources representing text notes with URIs and metadata
Tools for creating new notes
Prompts for generating summaries of notes
Features
Resources
List and access notes via
note://URIsEach note has a title, content and metadata
Plain text mime type for simple content access
Tools
create_note- Create new text notesTakes title and content as required parameters
Stores note in server state
Prompts
summarize_notes- Generate a summary of all stored notesIncludes all note contents as embedded resources
Returns structured prompt for LLM summarization
Related MCP server: filesystem-mcp
Development
Install dependencies:
npm installBuild the server:
npm run buildFor development with auto-rebuild:
npm run watchInstallation
Installing via Smithery
To install Gmail Notes Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @mundume/gmail-mcp --client claudeInstalling Manually
To use with Claude Desktop, add the server config:
On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"gmail-mcp": {
"command": "/path/to/gmail-mcp/build/index.js"
}
}
}Debugging
Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:
npm run inspectorThe Inspector will provide a URL to access debugging tools in your browser.
Available Tools
3 toolsgetEmailContentC
Retrieve the full content of an email from Gmail.
Inputs: emailIndex (required).
listEmailsB
List emails from Gmail with subject, sender, and body in Markdown format. Optionally filter and summarize results.
Inputs: query, maxResults.
sendEmailC
Send an email from Gmail.
Inputs: to (required), subject (required), body (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: getEmailContent retrieves a specific email's full content, listEmails lists emails with optional filtering/summarization, and sendEmail sends emails. There is no overlap in functionality, making tool selection straightforward for an agent.
All tools follow a consistent verb_noun naming pattern (getEmailContent, listEmails, sendEmail) with clear, descriptive names. The naming style is uniform throughout the set, enhancing readability and predictability.
With only 3 tools, the set feels thin for a Gmail integration, as it lacks operations like deleting emails, marking as read/unread, or managing labels. While the core functions are present, the scope is limited compared to typical email management needs.
The tools cover basic read (get, list) and write (send) operations, but there are notable gaps in the email lifecycle, such as updating email status (e.g., delete, archive, mark as read) or handling attachments. This could lead to agent workarounds for common email tasks.
Maintenance
Related MCP Connectors
Google Keep-style notes app with an MCP server for AI agents to read/write notes.
An MCP server that used to create notes
Markdown-based note-taking with a hosted MCP server. Your notes serve you and your AI.
Nifty's MCP server — exposes tasks, projects, messages, and files as tools for AI agents.
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