Enables email functionality with Gmail accounts, supporting email search, reading email content with threading, viewing statistics, and composing/sending emails securely with TLS.
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., "@ClaudePost show me emails from yesterday"
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.
A Model Context Protocol (MCP) server that provides a seamless email management interface through Claude. This integration allows you to handle emails directly through natural language conversations with Claude, supporting features like searching, reading, and sending emails securely.
📧 Search emails by date range and keywords
📅 View daily email statistics
📝 Read full email content with threading support
✉️ Send emails with CC recipients support
🔒 Secure email handling with TLS
Related MCP server: IMAP MCP Server
Python 3.12 or higher
A Gmail account (or other email provider)
If using Gmail:
Two-factor authentication enabled
App-specific password generated
Claude Desktop application
Install uv:
# MacOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
# Remember to restart your terminal after installationClone and set up the project:
# Clone the repository
git clone https://github.com/ZilongXue/claude-post.git
cd claude-post
# Create and activate virtual environment
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
# Install dependencies
uv pip install -e .Create a .env file in the project root:
EMAIL_ADDRESS=your.email@gmail.com
EMAIL_PASSWORD=your-app-specific-password
IMAP_SERVER=imap.gmail.com
SMTP_SERVER=smtp.gmail.com
SMTP_PORT=587Configure Claude Desktop:
First, make sure you have Claude for Desktop installed. You can install the latest version here. If you already have Claude for Desktop, make sure it's updated to the latest version.
Open your Claude Desktop configuration file:
# MacOS
~/Library/Application Support/Claude/claude_desktop_config.json
# Create the file if it doesn't exist
mkdir -p ~/Library/Application\ Support/Claude
touch ~/Library/Application\ Support/Claude/claude_desktop_config.jsonAdd the following configuration:
{
"mcpServers": {
"email": {
"command": "/Users/username/.local/bin/uv",
"args": [
"--directory",
"/path/to/claude-post/src/email_client",
"run",
"email-client"
]
}
}
}Replace /Users/username and /path/to/claude-post with your actual paths.
After updating the configuration, restart Claude Desktop for the changes to take effect.
The server runs automatically through Claude Desktop:
The server will start when Claude launches if configured correctly
No manual server management needed
Server stops when Claude is closed
You can interact with your emails using natural language commands. Here are some examples:
"Show me emails from last week"
"Find emails with subject containing 'meeting'"
"Search for emails from recruiting@linkedin.com between 2024-01-01 and 2024-01-07"
"Search sent emails from last month"
"Show me the content of email #12345"
"What's the full message of the last email from HR?"
"How many emails did I receive today?"
"Show me daily email counts for the past week"
"I want to send an email to john@example.com"
"Send a meeting confirmation to team@company.com"
Note: For security reasons, Claude will always show you the email details for confirmation before actually sending.
claude-post/
├── pyproject.toml
├── README.md
├── LICENSE
├── .env # Not included in repo
├── .python-version # Python version specification
└── src/
└── email_client/
├── __init__.py
├── __main__.py
└── server.py # Main implementationUse app-specific passwords instead of your main account password
For Gmail users:
Enable 2-Step Verification in your Google Account
Generate an App Password for this application
Use the App Password in your .env file
The application logs detailed information to email_client.log. Check this file for debugging information and error messages.
This project is licensed under the MIT License - see the LICENSE file for details.
Count emails received for each day in a date range
| Name | Required | Description | Default |
|---|---|---|---|
| end_date | Yes | End date in YYYY-MM-DD format | |
| start_date | Yes | Start date in YYYY-MM-DD format |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden. It does not disclose scope (e.g., all folders, spam), side effects, rate limits, or output format. Minimal behavioral context beyond the basic 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?
Single sentence, zero waste, front-loaded with verb and resource.
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?
Missing output format (array of date-count objects), scope (which mailbox), and assumptions about missing days. No output schema to compensate.
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?
Schema coverage is 100%, so baseline is 3. Description adds no extra meaning beyond schema; it implies date range but doesn't clarify inclusive/exclusive bounds or format validation.
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 'Count emails received for each day in a date range' uses a specific verb ('count') and resource ('emails') and clearly distinguishes from sibling tools like 'search-emails' or 'send-email'.
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?
No guidance on when to use this tool versus alternatives. It does not mention when not to use it or compare to other tools like 'search-emails' for broader queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the full content of a specific email by its ID
| Name | Required | Description | Default |
|---|---|---|---|
| email_id | Yes | The ID of the email to retrieve |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must carry the full burden. It says 'full content' but does not specify whether that includes attachments, headers, or metadata. No mention of permissions, rate limits, or destructive potential.
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 a single, clear sentence with no redundant words. It is front-loaded with the action and resource.
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 low complexity (1 parameter, no output schema), the description is minimally adequate but lacks details on what 'full content' includes. It does not compensate for the missing output schema.
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?
Schema coverage is 100% and the description adds no additional meaning beyond what the schema already provides for email_id. It does not specify the format or source of the ID.
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 action (Get), resource (full content of an email), and the identifier (by its ID). It distinguishes from siblings like search-emails (which lists) and forward-email (which sends).
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?
No guidance on when to use this tool versus alternatives, such as after using search-emails to find an email ID, or what prerequisites exist (e.g., email must exist).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search emails within a date range and/or with specific keywords
| Name | Required | Description | Default |
|---|---|---|---|
| end_date | No | End date in YYYY-MM-DD format (optional) | |
| folder | No | Folder to search in ('inbox' or 'sent', defaults to 'inbox') | |
| keyword | No | Keyword to search in email subject and body (optional) | |
| start_date | No | Start date in YYYY-MM-DD format (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions search functionality but lacks critical details: it doesn't specify whether this is a read-only operation, what permissions are required, how results are returned (e.g., pagination, format), or any rate limits. For a search tool with zero annotation coverage, this is a significant gap.
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 a single, efficient sentence that front-loads the core purpose ('search emails') and succinctly lists the search criteria. There's no wasted verbiage or redundancy, making it easy for an agent to parse quickly.
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 lack of annotations and output schema, the description is incomplete for a search tool. It doesn't explain what the tool returns (e.g., email summaries, IDs, full content), how results are structured, or any behavioral constraints. This leaves the agent with insufficient context to use the tool effectively beyond basic parameter input.
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?
Schema description coverage is 100%, with all parameters well-documented in the input schema (e.g., date formats, folder options, keyword usage). The description adds minimal value beyond the schema by implying date-range and keyword filtering but doesn't provide additional syntax or format details. This meets the baseline for high schema coverage.
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 tool's purpose with specific verbs ('search emails') and resources ('emails'), and specifies search criteria ('within a date range and/or with specific keywords'). However, it doesn't explicitly differentiate from sibling tools like 'count-daily-emails' or 'get-email-content', which prevents a perfect score.
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 like 'count-daily-emails' (for counting) or 'get-email-content' (for retrieving specific content). There's no mention of prerequisites, exclusions, or comparative use cases, leaving the agent to infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
CONFIRMATION STEP: Actually send the email after user confirms the details. Before calling this, first show the email details to the user for confirmation. Required fields: recipients (to), subject, and content. Optional: CC recipients.
| Name | Required | Description | Default |
|---|---|---|---|
| cc | No | List of CC recipient email addresses (optional, confirmed) | |
| content | Yes | Confirmed email content | |
| subject | Yes | Confirmed email subject | |
| to | Yes | List of recipient email addresses (confirmed) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the confirmation requirement and required/optional fields, which are behavioral traits. However, it doesn't mention other important aspects like authentication needs, rate limits, error handling, or what happens after sending (e.g., success confirmation, delivery status). For a mutation tool with zero annotation coverage, this leaves significant gaps.
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 appropriately sized and front-loaded with the most critical information (confirmation step and required fields). Every sentence earns its place by providing essential workflow guidance and parameter information without redundancy or unnecessary elaboration.
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 this is a mutation tool with no annotations and no output schema, the description should do more. It covers the confirmation workflow and parameter basics adequately, but lacks information about what happens after sending (response format, success indicators, error conditions). For a tool that performs an irreversible action like sending emails, this represents a meaningful gap in completeness.
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?
Schema description coverage is 100%, so the schema already fully documents all 4 parameters. The description adds minimal value by listing required fields (to, subject, content) and optional CC recipients, but doesn't provide additional semantic context beyond what's in the schema descriptions (e.g., format expectations, constraints). Baseline 3 is appropriate when the schema does the heavy lifting.
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 ('send the email') and resource ('email'), distinguishing it from sibling tools like count-daily-emails, get-email-content, and search-emails which are read-only operations. It explicitly mentions the tool's role in the email workflow.
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 explicit guidance on when to use this tool: 'after user confirms the details' and 'Before calling this, first show the email details to the user for confirmation.' It clearly establishes a prerequisite workflow step, distinguishing it from alternatives that might send emails without confirmation.
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.
v1.0.0count-daily-emailsget-email-contentsearch-emailssend-emailScored across 4 tools
Each tool has a clearly distinct purpose with no overlap: count-daily-emails aggregates email statistics, get-email-content retrieves a specific email's full content, search-emails finds emails based on criteria, and send-email handles email composition and sending. The descriptions clearly differentiate their functions, making misselection unlikely.
The tool names follow a consistent verb-noun pattern with hyphens (e.g., count-daily-emails, get-email-content), which is readable and predictable. The minor deviation is that send-email uses a simpler noun form (email instead of email-content or similar), but this does not break the overall consistency significantly.
With 4 tools, this server is well-scoped for email management, covering key operations: counting, retrieving, searching, and sending emails. Each tool earns its place by addressing a distinct aspect of the domain without being overly sparse or bloated.
The tool set provides good coverage for core email workflows, including read (count, get, search) and write (send) operations. A minor gap is the lack of update or delete tools for managing existing emails, but agents can work around this, and the surface is largely complete for the stated purpose.
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