The AVA MCP Server enables AI applications to create email drafts using the Gmail API. It integrates with the Model Context Protocol (MCP) to provide the following capabilities:
Create Email Drafts: Specify recipients, subject, and body content for draft emails in Gmail
Google OAuth Integration: Securely authenticates with Gmail using OAuth credentials and permissions
Customization: Behavior can be personalized by updating details in the prompts/ava.md file
Environment Configuration: Uses variables like USER_EMAIL and credential paths for authentication
Security: Protects sensitive authentication files from version control
Provides access to Gmail API, allowing the AI assistant (AVA) to read and manage emails through the Gmail service.
Integrates with Google services through OAuth authentication, enabling secure access to Google-based features for the virtual assistant.
Utilizes Google Cloud Platform for API access and authentication, supporting the virtual assistant's functionality through Google Cloud services.
Click on "Install 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., "@AVA MCP Server check my unread emails and summarize the important ones"
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.
All credits to : https://github.com/ShawhinT/YouTube-Blog/
Fourth example in AI agents series. Here, I build a customer MCP server to give any AI app access to a toolset for an Artificial Virtual Assistant (AVA).
Links
Clone this repo
Install uv if you haven't already
# Mac/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
# Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"Test the server in dev mode
uv run mcp dev mcp-server-example.pyAdd server config to AI app (e.g. Claude Desktop or Cursor).
{
"mcpServers": {
"AVA": {
"command": "/Users/shawhin/.local/bin/uv", # replace with global path to your uv installation
"args": [
"--directory",
"/Users/shawhin/Documents/_code/_stv/sandbox/ava-mcp/", # replace with global path to repo
"run",
"mcp-server-example.py"
]
}
}
}Related MCP server: Gmail AutoAuth MCP Server
Locate the prompts/ava.md file in your project directory
Customize the file with:
Communication preferences
Specific instructions for handling tasks
Any other relevant guidelines for AVA
Create a .env file in the root directory of the project with the following variables:
USER_EMAIL=your_email_address
# Google OAuth Credentials
GOOGLE_CREDENTIALS_PATH=.config/ava-agent/credentials.json
GOOGLE_TOKEN_PATH=.config/ava-agent/token.jsonUSER_EMAIL: The Gmail address you want to use for this application
GOOGLE_CREDENTIALS_PATH: Path to your Google OAuth credentials file
GOOGLE_TOKEN_PATH: Path where the Google OAuth token will be stored
First, create the required directory structure:
mkdir -p .config/ava-agentGo to the Google Cloud Console
Create a new project or select an existing one
Enable the Gmail API:
In the navigation menu, go to "APIs & Services" > "Library"
Search for "Gmail API"
Click "Enable"
In the Google Cloud Console:
Go to "APIs & Services" > "Credentials"
Click "Create Credentials" > "OAuth client ID"
Choose "Desktop application" as the application type
Give it a name (e.g., "AVA Gmail Client")
Click "Create"
Download the credentials:
After creation, click "Download JSON"
Save the downloaded file as credentials.json in .config/ava-agent/
The file should contain your client ID and client secret
In the Google Cloud Console:
Go to "APIs & Services" > "OAuth consent screen"
Choose "External" user type
Fill in the required information:
App name
User support email
Developer contact information
Add the Gmail API scope: https://www.googleapis.com/auth/gmail.modify
Add your email as a test user
Complete the configuration
Before the server can access you Gmail account you will need to authorize it. You can do this by running uv run oauth.py which does the following.
Check for the presence of token.json
If not found, it will initiate the Google OAuth authentication flow
Guide you through the authentication process in your browser:
You'll be asked to sign in to your Google account
Grant the requested permissions
The application will automatically save the token
Generate and store the token automatically
Never commit your .env file or token.json to version control
Keep your Google credentials secure
Add the following to your .gitignore:
.env
.config/ava-agent/token.json
.config/ava-agent/credentials.jsonCreate a draft email using the Gmail API.
Args:
recipient_email (str): The email address of the recipient.
subject (str): The subject line of the email.
body (str): The main content/body of the email.
Returns:
dict or None: A dictionary containing the draft information including 'id' and 'message'
if successful, None if an error occurs.
Raises:
HttpError: If there is an error communicating with the Gmail API.
Note:
This function requires:
- Gmail API credentials to be properly configured
- USER_EMAIL environment variable to be set with the sender's email address
- Appropriate Gmail API permissions for creating drafts
| Name | Required | Description | Default |
|---|---|---|---|
| recipient_email | Yes | ||
| subject | Yes | ||
| body | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the operation's purpose, return values (success dictionary with id/message or None), error conditions (HttpError), and prerequisites (credentials, environment variable, permissions). It doesn't mention rate limits, retry behavior, or concurrency considerations, but covers the essential behavioral aspects for a write operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (Args, Returns, Raises, Note) and efficiently conveys necessary information. While slightly longer than minimal, each section earns its place by providing valuable context. The information is front-loaded with the core purpose stated first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (write operation with API dependencies), no annotations, and no output schema, the description does a good job of providing context. It explains the operation, parameters, return values, errors, and prerequisites. While it could mention more about the Gmail API context or provide examples, it covers the essential information needed to understand and use the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must compensate for the lack of parameter documentation in the schema. It provides clear parameter descriptions in the Args section, explaining what each parameter represents (recipient_email, subject, body). While it doesn't specify format constraints or examples, it adds substantial meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Create a draft email') and resource ('using the Gmail API'), with no sibling tools to differentiate from. It provides a complete verb+resource+scope statement that leaves no ambiguity about what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. While it mentions prerequisites (Gmail API credentials, environment variable, permissions), it doesn't indicate scenarios where this tool is appropriate or when other email-related tools might be preferred. No explicit when/when-not/alternatives information is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
write_email_draftWith only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is singular and clearly defined as creating email drafts via Gmail API.
The single tool name follows a clear verb_noun pattern (write_email_draft). With only one tool, consistency is inherently perfect as there are no other names to compare against.
A single tool is insufficient for an email/Gmail server's scope. Basic email operations like sending, reading, listing, or deleting emails are missing, making this server feel incomplete and thin for its apparent domain.
The server is severely incomplete for email management. While write_email_draft covers draft creation, it lacks essential operations like send_email, list_emails, get_email, delete_email, or manage labels, creating significant gaps that will cause agent failures.
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