Skip to main content
Glama
deenesjakoruzh
jnpacker

Gmail MCP Server

by jnpacker
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Gmail MCP Server

A purpose-built Model Context Protocol (MCP) server for Gmail integration, allowing AI assistants to review unread emails and perform email management operations.

Features

  • List Unread Emails: Retrieve unread emails from Gmail inbox with optional subject filtering

  • List All Emails: Retrieve all emails from Gmail (defaults to inbox, option for all mail)

  • Search Emails: Search emails using full Gmail query syntax (from:, to:, subject:, has:attachment, after:, label:, is:starred)

  • Email Content: Access complete email content including headers, body, and metadata

  • Delete Emails: Permanently delete emails by ID

  • Archive Emails: Archive emails (remove from inbox) by ID

  • Web Dashboard: Beautiful, responsive dashboard for intelligent inbox management

  • Auto-Triage: Automatic email classification and organization every 15 minutes

  • Auto-Cleanup: Intelligent deletion of trivial emails and archiving of calendar invites

Related MCP server: Gmail MCP Server

Installation

  1. Clone this repository:

git clone <repository-url>
cd gmail-mcp-server

  1. Set up Google OAuth 2.0 credentials:

    • Go to Google Cloud Console

    • Create a new project or select an existing one

    • Enable the Gmail API

    • Create OAuth 2.0 credentials (Desktop application)

    • Download the credentials JSON file and save as credentials.json in the project root

  2. Authenticate (see Authentication below):

make auth

No separate install step is needed — make auth (and every other make target that needs Python deps, e.g. test, lint, dashboard) automatically creates a local .venv/ and installs the project into it on first run. You never need to pip install anything system-wide (many distros ship an "externally managed" system Python that refuses direct pip install anyway).

Start the server:

.venv/bin/python -m gmail_mcp_server.server

Web Dashboard & Inbox Management

The Gmail MCP Server includes a powerful web-based dashboard for intelligent inbox management with automatic triaging and organization.

Quick Start

Start the dashboard with:

make dashboard

Or manually:

.venv/bin/python app.py

The dashboard will be available at http://localhost:5000

Dashboard Features

  • Auto-Triage Every 15 Minutes: Automatically classifies and organizes emails

  • Intelligent Organization: Groups emails by priority (Critical → Important → Info)

  • Auto-Cleanup: Automatically deletes trivial field changes and archives calendar invites

  • Real-time Stats: View total emails, last sync time, and next sync countdown

  • Quick Navigation: Click email groups to preview Gmail search results

  • Responsive Design: Works on desktop, tablet, and mobile devices

  • Manual Refresh: Trigger triage immediately with the refresh button

Using with Claude Code

When using Claude Code, you can leverage this Gmail MCP server to manage your email directly from your development environment:

  1. Inbox Triaging: Use the /triage command to automatically organize and clean your inbox

  2. Integration in Workflows: Claude Code can help analyze email content and suggest actions

  3. Automated Management: Set up the dashboard to run in the background and manage emails while you code

  4. Easy Access: Check your organized inbox without leaving your IDE

To use with Claude Code:

  1. Ensure the MCP server is configured in your .mcp.json

  2. Claude Code will have access to the Gmail tools for email management

  3. Use natural language commands to manage emails (e.g., "delete these spam emails", "archive calendar invites")

See DASHBOARD.md for comprehensive dashboard documentation.

MCP Configuration

To use this Gmail MCP server with Claude or gemini-cli, you need to configure a .mcp.json file. This file tells the AI assistant how to connect to your MCP server.

.mcp.json Configuration

Create a .mcp.json file in your home directory or project directory with the following configuration:

{
  "mcpServers": {
    "gmail": {
      "command": "/path/to/gmail-mcp-server/.venv/bin/python3",
      "args": ["-m", "gmail_mcp_server.server"],
      "cwd": "/path/to/gmail-mcp-server"
    }
  }
}

Configuration Details:

  • command: The Python interpreter to use. Point this at .venv/bin/python3 (created automatically by make auth) so the server has access to its installed dependencies — a bare python/python3 will fail with ModuleNotFoundError unless those packages happen to be installed system-wide.

  • args: Arguments to pass to the Gmail MCP server module

  • cwd: The working directory where the Gmail MCP server is installed

For Claude Desktop: Place the .mcp.json file in your Claude Desktop configuration directory:

  • macOS: ~/Library/Application Support/Claude/

  • Windows: %APPDATA%\Claude\

  • Linux: ~/.config/claude/

For gemini-cli: Place the .mcp.json file in your home directory or specify the path when running gemini-cli.

Example Usage

Once configured, you can use the Gmail MCP server with AI assistants by passing it in your client configuration.

Dashboard PIN Security

The dashboard can be protected with a 4-digit PIN. When configured, the dashboard shows a PIN entry screen on every new session (sessions last 4 hours).

Setting a PIN

make set-pin
# Enter new PIN: ****
# Confirm PIN: ****
# PIN saved.

Or use the Python CLI directly:

python3 app.py --set-pin

This writes a PBKDF2-SHA256 hashed PIN to .pincode in the project root. The raw PIN is never stored. Both .pincode and .flask_secret are gitignored.

To remove PIN protection, delete .pincode:

rm .pincode

Running in Kubernetes

All secrets are consolidated in a single gmail-mcp-secrets Kubernetes Secret (see k8s/secret.yaml_example). When using PIN protection, include the pre-hashed .pincode value there rather than generating it on-disk.

1. Generate the PIN hash locally:

make set-pin        # writes .pincode to repo root
cat .pincode        # copy the "salt:hash" string

Or generate it directly:

python3 -c "
import secrets, hashlib
pin = '1234'  # replace with your PIN
salt = secrets.token_hex(16)
h = hashlib.pbkdf2_hmac('sha256', pin.encode(), salt.encode(), 260000).hex()
print(f'{salt}:{h}')
"

2. Add it to your k8s/secret.yaml (alongside the other secrets):

stringData:
  .pincode: "salt:hash-from-above"
  FLASK_SECRET_KEY: "$(python3 -c 'import secrets; print(secrets.token_hex(32))')"
  # ... other fields from k8s/secret.yaml_example

3. Apply and deploy:

kubectl apply -f k8s/secret.yaml
kubectl apply -f k8s/deployment.yaml

The entrypoint copies .pincode from the read-only /secrets/ mount to /app/ on startup. FLASK_SECRET_KEY is injected as an environment variable to keep sessions stable across pod restarts.

Make Commands

Use the included Makefile for quick access to common tasks:

# Display available commands
make help

# Initialize Gmail OAuth authentication (requires credentials.json)
make auth

# Set or change the dashboard PIN
make set-pin

# Start the web dashboard
make dashboard

# Stop the running dashboard
make kill-dashboard

# Run inbox triage once (email classification and organization)
make triage

# Watch inbox every 10 minutes (runs triage repeatedly)
make watch

You can specify which Claude model to use with the MODEL variable:

make triage MODEL=haiku        # Fast triage with Haiku (default)
make triage MODEL=sonnet       # Balanced triage with Sonnet
make triage MODEL=opus         # Most capable triage with Opus
make watch MODEL=opus

Available Tools

1. list_unread_emails

Lists unread emails in Gmail inbox with optional filtering. Rebuilds the in-memory position map used by delete/archive/modify tools.

Parameters:

  • subject_filter (optional): Filter emails by subject text

  • max_results (optional): Maximum number of emails to return (default: 50)

2. list_all_emails

Lists emails in Gmail (defaults to inbox, including both read and unread messages). Rebuilds the in-memory position map.

Parameters:

  • inbox_only (optional): Whether to list only emails currently in inbox (default: true). Set to false to list all emails across all folders.

  • max_results (optional): Maximum number of emails to return (default: 50)

3. search_emails

Searches emails using standard Gmail search query syntax. Rebuilds the in-memory position map.

Parameters:

  • query (required): Gmail search query string (e.g. from:user@example.com, has:attachment, subject:report, after:2024/01/01, is:starred, label:work)

  • max_results (optional): Maximum number of emails to return (default: 50)

4. delete_emails

Moves emails to trash and marks them as read. Accepts position numbers from the last email list/search call and/or explicit Gmail message IDs.

Parameters:

  • positions (optional): Array of 1-based position numbers from the email list

  • message_ids (optional): Array of Gmail message IDs

5. archive_emails

Archives emails (removes from inbox) and marks them as read.

Parameters:

  • positions (optional): Array of 1-based position numbers

  • message_ids (optional): Array of Gmail message IDs

6. list_labels

Returns all Gmail labels (system + user-defined).

Parameters: None

7. create_label

Creates a new Gmail label with optional color.

Parameters:

  • name (required): Label name (e.g., Triage/Security)

  • background_color (optional): Hex color (e.g., #4a86e8) — must be a predefined Gmail color

  • text_color (optional): Hex text color — must be paired with background_color

8. modify_labels

Adds and/or removes labels on emails. When adding a Triage/* label, all other Triage/* labels on the email are automatically removed (one-label-per-email invariant).

Parameters:

  • positions (optional): Array of 1-based position numbers

  • message_ids (optional): Array of Gmail message IDs

  • add_labels (optional): Array of label names to add

  • remove_labels (optional): Array of label names to remove

9. list_recent_actions

Returns the in-memory log of recent email operations (capped at 100).

Parameters:

  • limit (optional): Maximum number of actions to return (default: 10)

Authentication

Initial Setup

On first run, the server requires authentication. Use the provided authentication helper:

make auth

This automatically creates .venv (if it doesn't exist yet) and installs dependencies into it before running the auth flow, so no manual pip install step is required.

Or manually, using the project's virtualenv:

.venv/bin/python -m gmail_mcp_server.auth

This will:

  1. Check that credentials.json exists in the project root

  2. Open a browser window for OAuth 2.0 authentication

  3. Request permission to access your Gmail account

  4. Save the authentication token to token.json for future use

Getting Credentials

Before running make auth, you need to set up Google OAuth 2.0 credentials:

  1. Go to Google Cloud Console

  2. Create a new project or select an existing one

  3. Enable the Gmail API

  4. Create OAuth 2.0 credentials (Desktop application)

  5. Download the credentials JSON file and save as credentials.json in the project root

How It Works

  • The server checks for an existing authentication token (token.json) on startup

  • If the token exists and is valid, the server uses it automatically

  • If the token is expired but has a refresh token, it refreshes automatically

  • If no token exists, the server will request authentication using the make auth command

Required Gmail API Scopes

  • https://www.googleapis.com/auth/gmail.readonly - Read emails

  • https://www.googleapis.com/auth/gmail.modify - Delete and archive emails

Security Notes

  • Keep your credentials.json and token.json files secure

  • These files are automatically ignored by git

  • The server only requests minimal required permissions

  • All operations are performed through official Gmail API

Development

make test, make lint, make format, and make auth all automatically create .venv/ (with dev dependencies) on first run, so there's no separate setup step.

Run tests:

make test          # run all tests
make test-cov      # run with coverage report

Lint and format:

make lint          # check with ruff
make format        # auto-format and fix imports with ruff

Run the MCP server directly:

.venv/bin/python -m gmail_mcp_server        # short form (via __main__.py)
.venv/bin/python -m gmail_mcp_server.server # explicit
.venv/bin/gmail-mcp-server                  # installed entry point

Test the server interactively with the MCP Inspector:

npx @modelcontextprotocol/inspector .venv/bin/python3 -m gmail_mcp_server.server

Available Tools

7 tools
archive_emailsA

Archive emails (remove from inbox). Accepts positions[] from email list and/or message_ids[].

ParametersJSON Schema
NameRequiredDescriptionDefault
positionsNoPosition numbers from the email list
message_idsNoGmail message IDs

TDQS

A3.8/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description bears full responsibility. It states the tool removes emails from inbox but does not disclose whether the action is reversible, permission requirements, or potential side effects (e.g., label changes). For a mutation tool, this is insufficient transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single efficient sentence that front-loads the action and then concisely lists the accepted inputs. No extraneous words or repetitions; every phrase earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with two fully described parameters and no output schema, the description covers the essential purpose and input relationship. It could be enhanced by mentioning the return value (e.g., success status or count), but the current level is adequate for most use cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for both parameters, but the description adds value by noting positions come from an email list (linking to sibling tool list_unread_emails) and that positions and message_ids are alternatives. This contextual information enhances the schema's basic definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (archive emails) and the resource (remove from inbox), and it distinguishes from siblings like delete_emails by specifying it only removes from inbox. It also explicitly mentions the two input methods, making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for moving emails out of inbox but does not explicitly state when to use this tool vs alternatives like delete_emails or modify_labels. No exclusions or prerequisites are provided, leaving the agent to infer usage context from sibling names.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

create_labelC

Create a new Gmail label

ParametersJSON Schema
NameRequiredDescriptionDefault
nameYesThe label name to create
text_colorNoHex text color (e.g. '#ffffff'). Must be used with background_color. Only predefined Gmail colors are accepted.
background_colorNoHex background color (e.g. '#4a86e8'). Must be used with text_color. Only predefined Gmail colors are accepted.

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description bears full responsibility for behavioral disclosure. It merely states the action without revealing what happens upon success or failure (e.g., duplicate label behavior, color validation, return value). This is a significant gap for a creation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the key action. It could be slightly expanded with usage hints without losing conciseness, but it is not overly verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of an output schema and annotations, the description should provide more context about the tool's behavior, such as whether it returns the created label, any side effects, or error conditions. The current text is insufficient for a complete understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema provides 100% description coverage for all three parameters, so the description adds minimal value beyond the schema. It correctly implies that 'name' is the label name, but does not elaborate on color constraints or usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Create' and the resource 'Gmail label', making the tool's purpose straightforward. However, it does not explicitly distinguish it from sibling tools like 'modify_labels' or 'list_labels', though the name itself provides some differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. For example, it does not mention that 'modify_labels' could be used to update existing labels, nor does it specify prerequisites or context for creation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

delete_emailsA

Move emails to trash and mark as read. Accepts positions[] from email list and/or message_ids[].

ParametersJSON Schema
NameRequiredDescriptionDefault
positionsNoPosition numbers from the email list
message_idsNoGmail message IDs

TDQS

A3.9/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Without annotations, the description carries the burden of behavioral disclosure. It states the tool moves emails to trash and marks them as read, which are key effects. However, it does not mention whether the action is reversible (e.g., Gmail trash recovery) or any side effects like batch limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two short, front-loaded sentences with no unnecessary words. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple (delete), and the description covers the core action and parameters. However, it lacks details about return behavior, error handling, or batch limitations, which would be helpful given no output schema or annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already describes both parameters. The description adds value by clarifying that positions[] and message_ids[] can be used 'and/or' together, indicating they are alternative or complementary ways to specify emails.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Move emails to trash and mark as read,' specifying the verb (move/trash, mark) and resource (emails). It distinguishes from sibling tools like archive_emails (which archives rather than trashes) and list_unread_emails (which lists).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is used to delete emails but does not explicitly state when to use this tool versus alternatives like archive_emails or modify_labels. No when-not-to-use or prerequisite guidance is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_labelsB

List all Gmail labels

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.1/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only states 'List all Gmail labels', but does not confirm it is read-only, describe output format, or mention any rate limits or permissions. For a zero-annotation tool, this is insufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that is front-loaded and contains no unnecessary words. It is concise and to the point.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, no annotations, and zero parameters, the description is adequate for a simple list operation. However, it does not provide any context about pagination, result format, or relationship to sibling tools like create_label. It is minimally complete but not enriching.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are zero parameters, so schema coverage is 100% trivially. The description adds no additional meaning beyond the schema. Baseline for 0 params is 4, but the description is minimal and does not enrich the agent's understanding of the tool's behavior or output.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists all Gmail labels, which is a specific verb+resource. It distinguishes from siblings like list_unread_emails (lists emails) and list_recent_actions (actions, not labels). However, it does not explicitly differentiate from create_label or modify_labels, which operate on labels but are different actions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. For example, before creating a label with create_label, an agent might need to list existing labels to avoid duplicates, but this is not mentioned. No exclusions or context cues.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_recent_actionsB

Show recent actions taken on emails (delete, archive, label changes, etc.)

ParametersJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of recent actions to show (default: 20)

TDQS

B3.3/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It only states the tool shows recent actions, but does not disclose behavioral traits like authentication requirements, action types scope, time range, sorting, or any side effects. Significant lack of transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that is front-loaded with the verb and resource. No filler words; every part serves a purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple list tool with one parameter and no output schema, the description is adequate but not fully complete. It mentions action types, but lacks details on output format, sorting, time range, or pagination. Leaves some ambiguity for the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with one parameter 'limit' having a default and description. The description adds no additional meaning beyond what the schema provides. Baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('show') and the resource ('recent actions on emails'), with specific examples (delete, archive, label changes). This distinguishes it from sibling tools like list_unread_emails (which shows emails, not actions) and delete/archive tools (which perform actions).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool vs. alternatives. It does not mention when not to use it or provide any conditions. The context from sibling tools only implicitly implies viewing, but no clear usage instructions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_unread_emailsA

List unread emails in Gmail inbox with optional subject filtering

ParametersJSON Schema
NameRequiredDescriptionDefault
max_resultsNoMaximum number of emails to return (default: 50)
subject_filterNoOptional filter to search for emails with specific subject content

TDQS

A3.6/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description bears the burden of disclosing behavior. It indicates a read operation but does not explicitly state it is read-only, nor does it mention pagination, rate limits, or other behavioral traits. Basic transparency is achieved but gaps remain.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, front-loaded with key information, no wasted words. Perfectly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description should at least hint at what is returned (e.g., email metadata). It fails to mention return format, fields, or behavior on empty results. For a list tool, this is a significant gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds no additional meaning beyond what the schema already provides for each parameter. The mention of 'subject filtering' is redundant with the schema description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'list' and the resource 'unread emails in Gmail inbox' with an optional filter. It distinguishes itself from sibling tools like delete_emails and archive_emails.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for listing unread emails but does not explicitly state when to use this tool versus alternatives (e.g., when to use list_unread_emails vs list_recent_actions). No when-not guidance is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

modify_labelsA

Batch add/remove labels on emails. Accepts positions[] and/or message_ids[], plus add_labels[] and/or remove_labels[] (label names). When adding a Triage/* label, all other Triage/* labels on the email are automatically removed.

ParametersJSON Schema
NameRequiredDescriptionDefault
positionsNoPosition numbers from the email list
add_labelsNoLabel names to add
message_idsNoGmail message IDs
remove_labelsNoLabel names to remove

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description discloses key behavioral traits: batch operation, parameter flexibility, and the automatic removal of other Triage/* labels when adding one. However, it does not mention idempotency, error conditions, or side effects beyond labeling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (two sentences) and front-loaded with the main action. Every sentence adds value: first defines the operation, second specifies parameter usage and a critical behavioral rule.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (4 parameters, no output schema), the description covers the core operation and a notable edge case. It does not explain return values or error handling, but for a label mutation tool, the behavioral details are adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, but the description adds value by clarifying that positions[] and message_ids[] are alternative identifiers, and add_labels/remove_labels refer to label names. It also introduces the Triage/* auto-removal logic, which is not in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: batch add/remove labels on emails. It specifies the action (modify labels), resource (emails), and unique behavior (Triage/* auto-removal), distinguishing it from sibling tools like list_labels (read-only) and create_label (single label creation).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for batch label operations but lacks explicit when-to-use or when-not-to-use guidance. It does not mention alternatives or prerequisites, though the Triage/* rule provides a specific conditional guideline.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.7/5.0
Disambiguation5/5

Each tool serves a unique function: listing unread emails, deleting, archiving, managing labels, and viewing recent actions. No two tools have overlapping purposes; even delete_emails and archive_emails are clearly distinguished by their actions.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., list_unread_emails, create_label, modify_labels). The naming is predictable and makes the action-resource relationship clear.

Tool Count5/5

With 7 tools, the server is well-scoped for basic Gmail inbox management and label operations. Each tool addresses a necessary operation without redundancy or unnecessary complexity.

Completeness3/5

The tool set covers core inbox operations (list, delete, archive) and label management (list, create, modify), but lacks essential features like sending emails, reading full email content, searching beyond unread, or marking read/unread. Gaps exist for a full email workflow.

Maintenance

ActivityMaintained
ResponsivenessSyncing

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

  • Gmail MCP Server

    JBR90
    A
    license
    Not graded
    quality
    D
    maintenance
    Enables AI assistants to manage Gmail by reading unread emails with automatic classification, creating AI-generated draft replies, and saving drafts directly to Gmail through the Gmail API.
    215
    MIT

  • Gmail MCP Server

    RihardsJ
    F
    license
    Not graded
    quality
    D
    maintenance
    Enables AI assistants to interact with Gmail accounts for reading unread emails, creating draft replies with proper threading, and managing messages, with optional professional writing guidelines, templates, and Google Docs/Calendar integration.

  • Gmail AutoAuth MCP Server

    StephenLReed
    A
    license
    Not graded
    quality
    D
    maintenance
    Enables AI assistants to manage Gmail through natural language interactions, including sending, reading, searching emails, and managing labels with auto authentication support.
    20,627
    1
    MIT

  • Gmail AutoAuth MCP Server

    lowrykun
    A
    license
    Not graded
    quality
    D
    maintenance
    Enables AI assistants to manage Gmail through natural language, including sending, reading, searching, labeling emails, managing attachments, and performing thread operations.
    3
    MIT

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jnpacker/gmail-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server