MCP-Email-Verify
MCP 电子邮件验证
轻量级**模型上下文协议 (MCP)**服务器,可让您的 LLM 验证电子邮件地址。此工具使用 AbstractAPI 电子邮件验证 API 检查电子邮件格式、域名有效性和可交付性。非常适合将电子邮件验证集成到 Claude Desktop 等 AI 应用程序中。
什么是模型上下文协议(MCP)?
MCP 的核心是一种标准化协议,旨在简化 AI 模型与外部系统之间的通信。MCP 可以被视为一种通用语言,允许不同的 AI 代理、工具和服务无缝交互。
特征
电子邮件验证:实时验证电子邮件地址。
MCP 集成:与 MCP 兼容的 LLM 无缝连接。
易于设置:使用 Python 和 MCP SDK 构建,可快速部署。
MCP 遵循客户端-服务器架构:
观看演示
点击下面的图片观看 MCP 电子邮件验证工具的视频演示:
Related MCP server: @bounceprotect/mcp
要求
Python :Python 3.11.0 或更高版本。
UV :0.6.9或更高。
设置
1.克隆存储库
git clone https://github.com/Abhi5h3k/MCP-Email-Verify.git
cd MCP-Email-Verify2.安装UV
如果您没有安装 UV,可以使用以下命令安装它:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"验证安装:
uv --version3.设置虚拟环境
使用 UV 创建虚拟环境:
uv venv激活虚拟环境:在 Windows 上:
.venv\Scripts\activate4.安装依赖项使用 UV 从 pyproject.toml 安装所需的依赖项:
uv install运行服务器
设置环境变量在根目录中创建一个 .env 文件并添加您的AbstractAPI密钥:
ABSTRACT_API_KEY=your_api_key_here运行服务器启动 MCP 服务器:
uv run server.py用法
使用 Claude Desktop 注册服务器更新 claude_desktop_config.json 文件以包含您的 MCP 服务器:
{
"mcpServers": {
"verify_mail": {
"command": "uv",
"args": [
"--directory",
"C:\\ABSOLUTE\\PATH\\TO\\MCP-Email-Verify",
"run",
"server.py"
]
}
}
}重新启动 Claude Desktop 重新启动 Claude Desktop 以检测新工具。
验证电子邮件使用如下提示:
我试图通过thanos@snap.io给灭霸发邮件,请他恢复我最喜欢的电视节目,但我不确定这封邮件是否有效。你能帮我看看这是真的还是只是凭空想象吗?
发展
格式化和 Linting 该项目使用 black 和 isort 进行代码格式化和导入排序。
安装开发依赖项:
uv add black isort --dev格式化代码:
black .对导入进行排序:
isort .设置预提交
pre-commit install
pre-commit run --all-filesSmithery.ai 服务器上可用: MCP 电子邮件验证
文章:模型上下文协议 (MCP): AI 通信未来入门指南
Available Tools
1 toolverify_emailA
Validates an email address using an external email validation API.
This function checks the validity, deliverability, and other attributes of an email address.
It returns a detailed dictionary containing information about the email's format, domain,
and SMTP server.
Args:
email (str): The email address to validate.
Returns:
dict[str, Any]: A dictionary containing detailed validation results. The dictionary
includes the following keys:
- "email" (str): The email address being validated.
- "autocorrect" (str): Suggested autocorrection if the email is invalid or malformed.
- "deliverability" (str): The deliverability status of the email (e.g., "DELIVERABLE").
- "quality_score" (str): A score representing the quality of the email address.
- "is_valid_format" (dict): Whether the email is in a valid format.
- "value" (bool): True if the format is valid, False otherwise.
- "text" (str): A textual representation of the format validity (e.g., "TRUE").
- "is_free_email" (dict): Whether the email is from a free email provider.
- "value" (bool): True if the email is from a free provider, False otherwise.
- "text" (str): A textual representation (e.g., "TRUE").
- "is_disposable_email" (dict): Whether the email is from a disposable email service.
- "value" (bool): True if the email is disposable, False otherwise.
- "text" (str): A textual representation (e.g., "FALSE").
- "is_role_email" (dict): Whether the email is a role-based email (e.g., "admin@domain.com").
- "value" (bool): True if the email is role-based, False otherwise.
- "text" (str): A textual representation (e.g., "FALSE").
- "is_catchall_email" (dict): Whether the domain uses a catch-all email address.
- "value" (bool): True if the domain is catch-all, False otherwise.
- "text" (str): A textual representation (e.g., "FALSE").
- "is_mx_found" (dict): Whether MX records are found for the email domain.
- "value" (bool): True if MX records are found, False otherwise.
- "text" (str): A textual representation (e.g., "TRUE").
- "is_smtp_valid" (dict): Whether the SMTP server for the email domain is valid.
- "value" (bool): True if the SMTP server is valid, False otherwise.
- "text" (str): A textual representation (e.g., "TRUE").
Example:
>>> await verify_email("thanos@snap.io")
{
"email": "thanos@snap.io",
"autocorrect": "",
"deliverability": "UNDELIVERABLE",
"quality_score": "0.00",
"is_valid_format": {
"value": true,
"text": "TRUE"
},
"is_free_email": {
"value": false,
"text": "FALSE"
},
"is_disposable_email": {
"value": false,
"text": "FALSE"
},
"is_role_email": {
"value": false,
"text": "FALSE"
},
"is_catchall_email": {
"value": false,
"text": "FALSE"
},
"is_mx_found": {
"value": false,
"text": "FALSE"
},
"is_smtp_valid": {
"value": false,
"text": "FALSE"
}
}
Raises:
ValueError: If the API key is not found in the environment variables.
requests.exceptions.HTTPError: If the API request fails (e.g., 4xx or 5xx error).
Exception: For any other unexpected errors.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and delivers comprehensive behavioral disclosure. It explains the external API dependency, detailed return structure, example output, and specific error conditions (ValueError for missing API key, HTTPError for API failures, Exception for other errors). This goes well beyond basic functionality description.
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 (purpose, Args, Returns, Example, Raises) and front-loaded with the core functionality. While comprehensive, some sections like the detailed Returns explanation could be more concise, but overall it maintains good information density with minimal redundancy.
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 (external API integration, detailed return structure) and absence of both annotations and output schema, the description provides exceptional completeness. It covers purpose, parameter, return value structure with detailed key explanations, concrete example, and error conditions - leaving no significant gaps for the agent to understand tool behavior.
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 and only one parameter, the description fully compensates by clearly explaining the 'email' parameter as 'The email address to validate' in the Args section. However, it doesn't provide additional semantic context like format expectations or validation rules beyond what's implied by the tool's purpose.
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 ('validates', 'checks') and resources ('email address', 'external email validation API'). It distinguishes what the tool does by specifying it checks 'validity, deliverability, and other attributes' and returns 'detailed dictionary containing information about the email's format, domain, and SMTP server'.
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 implies usage context through the detailed example and return value explanation, suggesting it's for email validation scenarios. However, there are no explicit guidelines on when to use this tool versus alternatives (though no sibling tools exist), prerequisites, or limitations beyond the error handling section.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v1.0.0- First observed
verify_email
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single verify_email tool has a clearly distinct purpose focused on email validation, making disambiguation trivial.
With only one tool, naming consistency is inherently perfect. The tool name follows a clear verb_noun pattern (verify_email) that would be appropriate if more tools were added, though there are no other tools to compare against.
A single tool is generally too few for most server purposes, creating a thin surface. While email verification could be a narrow domain, typical MCP servers benefit from 3-15 tools for richer functionality. This minimal tool count limits agent capabilities significantly.
For the narrow domain of email verification, the single tool provides comprehensive validation functionality. However, there are notable gaps for broader email-related operations like bulk verification, list management, or integration with email sending services that might be expected from an email-focused server.
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