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abstractapi-mcp-server

by avivshafir

抽象 API MCP 服务器

模型上下文协议 (MCP) 服务器,使用抽象 API 服务提供电子邮件和电话验证工具。该服务器基于 FastMCP 构建,可轻松将验证功能集成到 AI 应用程序和工作流程中。

概述

该 MCP 服务器公开了三个主要验证工具:

  • 电子邮件验证:全面的电子邮件地址验证和确认

  • 电话验证:190多个国家的电话号码验证

  • 电子邮件信誉:具有安全洞察的高级电子邮件信誉分析

Related MCP server: revenuebase-mcp-server

特征

电子邮件验证

  • 格式验证

  • 可交付性检查

  • 域验证

  • SMTP 验证

  • 检测一次性/角色/通用电子邮件

  • 质量评分

电话验证

  • 国际电话号码验证

  • 格式标准化(国际/本地)

  • 国家和运营商识别

  • 电话类型检测(手机、座机等)

  • 位置信息

电子邮件信誉

  • 全面的可交付性分析

  • 质量评分和风险评估

  • 发件人和组织身份证明

  • 域名安全分析(DMARC、SPF)

  • 数据泄露历史追踪

  • 欺诈和滥用检测

先决条件

  • Python 3.11+

  • uv (快速 Python 包安装程序)

  • 抽象 API 密钥(在abstractapi.com获取)

安装

选项 1:使用 uv(推荐)

  1. 克隆存储库:

git clone https://github.com/avivshafir/abstractapi-mcp-server
cd abstractapi-mcp-server
  1. 创建虚拟环境并安装依赖项:

uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install .
  1. 设置环境变量:

cp .env.example .env
# Edit .env and add your Abstract API key

选项 2:使用传统 pip

  1. 克隆存储库:

git clone https://github.com/avivshafir/abstractapi-mcp-server
cd abstractapi-mcp-server
  1. 创建虚拟环境:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
  1. 安装依赖项:

pip install -r requirements.txt
  1. 设置环境变量:

cp .env.example .env
# Edit .env and add your Abstract API key

您的.env文件应包含:

ABSTRACT_API_KEY=your_abstract_api_key_here

用法

运行 MCP 服务器

该服务器可以在 stdio 模式下运行,以便与 MCP 客户端集成:

# With uv (if virtual environment is activated)
python server.py

# Or run directly with uv
uv run server.py

FastMCP 框架

此服务器使用FastMCP构建,这是一个简化 MCP 服务器开发的 Python 框架。FastMCP 提供以下功能:

  • 自动工具注册:使用@mcp.tool()修饰的函数将自动公开为 MCP 工具

  • 类型安全:完整的类型提示和验证

  • 轻松异步支持:原生异步/等待支持

  • 简化的服务器设置:最少的样板代码

FastMCP 关键概念

from mcp.server.fastmcp import FastMCP

# Initialize the server
mcp = FastMCP("abstract_api")

# Register a tool
@mcp.tool()
async def my_tool(param: str) -> dict:
    """Tool description for AI clients"""
    return {"result": param}

# Run the server
mcp.run(transport="stdio")

可用工具

1. 电子邮件验证( verify_email

验证电子邮件地址并返回综合信息。

参数:

  • email (str):需要验证的电子邮件地址

响应示例:

{
  "email": "user@example.com",
  "deliverability": "DELIVERABLE",
  "quality_score": "0.99",
  "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": true, "text": "TRUE"},
  "is_smtp_valid": {"value": true, "text": "TRUE"}
}

2. 电话验证( validate_phone

验证来自 190 多个国家的电话号码。

参数:

  • phone (str): 需要验证的电话号码

  • country (str,可选):上下文的 ISO 国家代码

响应示例:

{
  "phone": "14152007986",
  "valid": true,
  "format": {
    "international": "+14152007986",
    "local": "(415) 200-7986"
  },
  "country": {
    "code": "US",
    "name": "United States",
    "prefix": "+1"
  },
  "location": "California",
  "type": "mobile",
  "carrier": "T-Mobile USA, Inc."
}

3. 电子邮件信誉( check_email_reputation

提供全面的电子邮件信誉分析,包括安全见解和违规历史。

参数:

  • email (str):要分析的电子邮件地址

响应示例:

{
  "email_address": "benjamin.richard@abstractapi.com",
  "email_deliverability": {
    "status": "deliverable",
    "status_detail": "valid_email",
    "is_format_valid": true,
    "is_smtp_valid": true,
    "is_mx_valid": true,
    "mx_records": ["gmail-smtp-in.l.google.com", "..."]
  },
  "email_quality": {
    "score": 0.8,
    "is_free_email": false,
    "is_username_suspicious": false,
    "is_disposable": false,
    "is_catchall": true,
    "is_subaddress": false,
    "is_role": false,
    "is_dmarc_enforced": true,
    "is_spf_strict": true,
    "minimum_age": 1418
  },
  "email_sender": {
    "first_name": "Benjamin",
    "last_name": "Richard",
    "email_provider_name": "Google",
    "organization_name": "Abstract API",
    "organization_type": "company"
  },
  "email_domain": {
    "domain": "abstractapi.com",
    "domain_age": 1418,
    "is_live_site": true,
    "registrar": "NAMECHEAP INC",
    "date_registered": "2020-05-13",
    "date_expires": "2025-05-13",
    "is_risky_tld": false
  },
  "email_risk": {
    "address_risk_status": "low",
    "domain_risk_status": "low"
  },
  "email_breaches": {
    "total_breaches": 2,
    "date_first_breached": "2018-07-23T14:30:00Z",
    "date_last_breached": "2019-05-24T14:30:00Z",
    "breached_domains": [
      {"domain": "apollo.io", "date_breached": "2018-07-23T14:30:00Z"},
      {"domain": "canva.com", "date_breached": "2019-05-24T14:30:00Z"}
    ]
  }
}

与 MCP 客户端集成

将此服务器添加到您的 mcp 配置中:

{
  "mcpServers": {
    "abstract-api": {
      "command": "uv",
      "args": ["run", "/path/to/mcp-abstract-api/server.py"],
      "env": {
        "ABSTRACT_API_KEY": "your_api_key_here"
      }
    }
  }
}

或者,如果您更喜欢使用传统方法:

{
  "mcpServers": {
    "abstract-api": {
      "command": "python",
      "args": ["/path/to/mcp-abstract-api/server.py"],
      "env": {
        "ABSTRACT_API_KEY": "your_api_key_here"
      }
    }
  }
}

其他 MCP 客户端

该服务器遵循标准 MCP 协议,可与任何兼容 MCP 的客户端集成。服务器通过 stdio 传输进行通信。

错误处理

该服务器包括全面的错误处理:

  • API 密钥验证:检查是否缺少 API 密钥

  • HTTP 错误处理:正确处理 API 响应错误

  • 输入验证:类型检查和参数验证

  • 优雅降级:用于调试的有意义的错误消息

API 速率限制

根据您的计划,抽象 API 具有不同的速率限制:

  • 免费计划:每秒 1 个请求

  • 付费计划:提供更高的费率限制

无论验证成功还是失败,每次 API 调用都算作一个积分。

发展

项目结构

mcp-abstract-api/
├── server.py          # Main MCP server implementation
├── .env              # Environment variables (not in repo)
├── .env.example      # Environment template
├── requirements.txt  # Python dependencies (pip format)
├── uv.lock           # uv lock file for reproducible builds
├── pyproject.toml    # Project configuration
├── README.md         # This file
└── LICENSE          # MIT License

添加新工具

要添加新的抽象 API 工具:

  1. 将 API 端点 URL 添加为常量

  2. 创建一个用@mcp.tool()装饰的新函数

  3. 添加包含参数和返回描述的综合文档字符串

  4. 按照现有模式实现错误处理

例子:

@mcp.tool()
async def new_validation_tool(param: str) -> dict[str, Any]:
    """
    Description of what this tool does.
    
    Args:
        param (str): Description of parameter
        
    Returns:
        dict[str, Any]: Description of return value
    """
    # Implementation here
    pass

贡献

  1. 分叉存储库

  2. 创建功能分支

  3. 进行更改

  4. 如果适用,添加测试

  5. 提交拉取请求

执照

该项目根据 MIT 许可证获得许可 - 有关详细信息,请参阅LICENSE文件。

支持

对于相关问题:

致谢

Available Tools

3 tools
check_email_reputationA
Analyzes email reputation using Abstract API's Email Reputation service.

This function provides comprehensive email reputation analysis including deliverability,
quality scoring, sender information, domain details, risk assessment, and breach history.
It's designed to help improve delivery rates, clean email lists, and block fraudulent users.

Args:
    email (str): The email address to analyze for reputation.

Returns:
    dict[str, Any]: A dictionary containing comprehensive reputation analysis. The dictionary
    includes the following main sections:
        - "email_address" (str): The email address that was analyzed.
        - "email_deliverability" (dict): Deliverability information.
            - "status" (str): "deliverable", "undeliverable", or "unknown".
            - "status_detail" (str): Additional detail (e.g., "valid_email", "invalid_format").
            - "is_format_valid" (bool): True if email follows correct format.
            - "is_smtp_valid" (bool): True if SMTP check was successful.
            - "is_mx_valid" (bool): True if domain has valid MX records.
            - "mx_records" (list): List of MX records for the domain.
        - "email_quality" (dict): Quality assessment information.
            - "score" (float): Confidence score between 0.01 and 0.99.
            - "is_free_email" (bool): True if from free provider (Gmail, Yahoo, etc.).
            - "is_username_suspicious" (bool): True if username appears auto-generated.
            - "is_disposable" (bool): True if from disposable email provider.
            - "is_catchall" (bool): True if domain accepts all emails.
            - "is_subaddress" (bool): True if uses subaddressing (user+label@domain.com).
            - "is_role" (bool): True if role-based address (info@, support@, etc.).
            - "is_dmarc_enforced" (bool): True if strict DMARC policy enforced.
            - "is_spf_strict" (bool): True if domain enforces strict SPF policy.
            - "minimum_age" (int|null): Estimated age of email address in days.
        - "email_sender" (dict): Sender information if available.
            - "first_name" (str|null): First name associated with email.
            - "last_name" (str|null): Last name associated with email.
            - "email_provider_name" (str|null): Email provider name (e.g., "Google").
            - "organization_name" (str|null): Organization linked to email/domain.
            - "organization_type" (str|null): Type of organization (e.g., "company").
        - "email_domain" (dict): Domain information.
            - "domain" (str): Domain part of the email.
            - "domain_age" (int|null): Age of domain in days.
            - "is_live_site" (bool|null): True if domain has active website.
            - "registrar" (str|null): Domain registrar name.
            - "registrar_url" (str|null): Registrar website URL.
            - "date_registered" (str|null): Domain registration date.
            - "date_last_renewed" (str|null): Last renewal date.
            - "date_expires" (str|null): Domain expiration date.
            - "is_risky_tld" (bool|null): True if top-level domain is considered risky.
        - "email_risk" (dict): Risk assessment.
            - "address_risk_status" (str): Risk level for the email address.
            - "domain_risk_status" (str): Risk level for the domain.
        - "email_breaches" (dict): Data breach information.
            - "total_breaches" (int|null): Number of known breaches.
            - "date_first_breached" (str|null): Date of first known breach.
            - "date_last_breached" (str|null): Date of most recent breach.
            - "breached_domains" (list): List of breached domains with dates.

Example:
    >>> await check_email_reputation("benjamin.richard@abstractapi.com")
    {
        "email_address": "benjamin.richard@abstractapi.com",
        "email_deliverability": {
            "status": "deliverable",
            "status_detail": "valid_email",
            "is_format_valid": true,
            "is_smtp_valid": true,
            "is_mx_valid": true,
            "mx_records": ["gmail-smtp-in.l.google.com", ...]
        },
        "email_quality": {
            "score": 0.8,
            "is_free_email": false,
            "is_username_suspicious": false,
            "is_disposable": false,
            "is_catchall": true,
            "is_subaddress": false,
            "is_role": false,
            "is_dmarc_enforced": true,
            "is_spf_strict": true,
            "minimum_age": 1418
        },
        "email_sender": {
            "first_name": "Benjamin",
            "last_name": "Richard",
            "email_provider_name": "Google",
            "organization_name": "Abstract API",
            "organization_type": "company"
        },
        "email_domain": {
            "domain": "abstractapi.com",
            "domain_age": 1418,
            "is_live_site": true,
            "registrar": "NAMECHEAP INC",
            "registrar_url": "http://www.namecheap.com",
            "date_registered": "2020-05-13",
            "date_last_renewed": "2024-04-13",
            "date_expires": "2025-05-13",
            "is_risky_tld": false
        },
        "email_risk": {
            "address_risk_status": "low",
            "domain_risk_status": "low"
        },
        "email_breaches": {
            "total_breaches": 2,
            "date_first_breached": "2018-07-23T14:30:00Z",
            "date_last_breached": "2019-05-24T14:30:00Z",
            "breached_domains": [
                {"domain": "apollo.io", "date_breached": "2018-07-23T14:30:00Z"},
                {"domain": "canva.com", "date_breached": "2019-05-24T14:30:00Z"}
            ]
        }
    }

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.
ParametersJSON Schema
NameRequiredDescriptionDefault
emailYes

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by disclosing behavioral traits: it explains the comprehensive analysis scope, mentions API dependencies (Abstract API), and includes error handling details in the 'Raises' section. However, it doesn't mention rate limits, authentication requirements beyond the API key error, or whether this is a read-only operation.

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

Conciseness3/5

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

The description is appropriately front-loaded with purpose and usage, but becomes overly verbose with an extremely detailed example (60+ lines) that duplicates information already implied by the return structure description. The 'Raises' section is useful but could be more concise.

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

Completeness5/5

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

Given the tool's complexity (comprehensive reputation analysis), no annotations, and no output schema, the description provides exceptional completeness: detailed purpose, parameter semantics, comprehensive return structure documentation, example output, and error handling. Nothing essential is missing for agent understanding.

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

Parameters5/5

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 compensates fully by providing detailed semantics for the 'email' parameter in the Args section, explaining it's 'The email address to analyze for reputation' with clear type information and usage context.

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 'analyzes email reputation using Abstract API's Email Reputation service' with specific verbs ('analyzes', 'provides comprehensive analysis') and distinguishes it from sibling tools (validate_phone, verify_email) by focusing on reputation analysis rather than validation or verification.

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 context ('designed to help improve delivery rates, clean email lists, and block fraudulent users') but doesn't explicitly state when to use this tool versus the sibling tools (validate_phone, verify_email). No explicit alternatives or exclusions are provided.

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

validate_phoneA
Validates a phone number using Abstract API's Phone Validation service.

This function checks the validity and other details of phone numbers from over 190 countries.
It returns detailed information about the phone number including format, country, location,
type, and carrier information.

Args:
    phone (str): The phone number to validate and verify.
    country (str, optional): The country's ISO code to indicate the phone number's country.
                            This helps the API append the corresponding country code to its analysis.
                            For example, use "US" for United States numbers.

Returns:
    dict[str, Any]: A dictionary containing detailed validation results. The dictionary
    includes the following keys:
        - "phone" (str): The phone number submitted for validation.
        - "valid" (bool): True if the phone number is valid, False otherwise.
        - "format" (dict): Object containing international and local formats.
            - "international" (str): International format with country code and "+" prefix.
            - "local" (str): Local/national format without international formatting.
        - "country" (dict): Object containing country details.
            - "code" (str): Two-letter ISO 3166-1 alpha-2 country code.
            - "name" (str): Name of the country where the phone number is registered.
            - "prefix" (str): Country's calling code prefix.
        - "location" (str): Location details (region, state/province, sometimes city).
        - "type" (str): Type of phone number. Possible values: "Landline", "Mobile",
                       "Satellite", "Premium", "Paging", "Special", "Toll_Free", "Unknown".
        - "carrier" (str): The carrier that the number is registered with.

Example:
    >>> await validate_phone("14152007986")
    {
        "phone": "14152007986",
        "valid": true,
        "format": {
            "international": "+14152007986",
            "local": "(415) 200-7986"
        },
        "country": {
            "code": "US",
            "name": "United States",
            "prefix": "+1"
        },
        "location": "California",
        "type": "mobile",
        "carrier": "T-Mobile USA, Inc."
    }

    >>> await validate_phone("2007986", "US")
    # Will validate with US country context

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.
ParametersJSON Schema
NameRequiredDescriptionDefault
phoneYes
countryNo

TDQS

A4.7/5.0
Behavior5/5

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 and does so comprehensively. It explains what the tool returns (detailed validation results), includes error handling information (raises section), describes the external API dependency, and provides a complete example of the return format. This goes well beyond basic functional description.

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 well-structured with clear sections (purpose, parameters, returns, example, raises) but is somewhat lengthy. Every section adds value, though some information could be more concise. The front-loaded purpose statement is clear, and the structure helps with comprehension despite the length.

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

Completeness5/5

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

For a tool with no annotations, no output schema, and 0% schema description coverage, the description provides exceptional completeness. It covers purpose, parameters, return values with detailed structure, examples, error handling, and external dependencies. The return value documentation effectively substitutes for a missing output schema, making this description highly complete.

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

Parameters5/5

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

With 0% schema description coverage, the description fully compensates by providing detailed parameter documentation. It explains both parameters thoroughly: 'phone' is the number to validate, and 'country' is an optional ISO code that helps with analysis. The description includes examples showing how both parameters work, adding significant value beyond the bare 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: 'Validates a phone number using Abstract API's Phone Validation service.' It specifies the exact action (validate), resource (phone number), and service provider, distinguishing it from sibling email tools. The description goes beyond the tool name by explaining it checks validity and returns detailed information.

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

Usage Guidelines4/5

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

The description provides clear context about when to use this tool: for validating phone numbers from over 190 countries. It doesn't explicitly mention when not to use it or compare with alternatives, but the context is sufficiently clear given the tool's specialized function. The examples show usage patterns with and without the optional country parameter.

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

verify_emailA
Validates an email address using an external email validation API of abstractapi.

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.
ParametersJSON Schema
NameRequiredDescriptionDefault
emailYes

TDQS

A4.3/5.0
Behavior4/5

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 tool's behavior: it uses an external API, returns detailed validation results, and includes error handling (raises exceptions for missing API key, HTTP errors, or other issues). It covers key aspects like what the tool does and potential failures, though it could add more on rate limits or performance.

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 well-structured with clear sections (purpose, args, returns, example, raises) and front-loaded key information. However, it includes an extensive example and detailed return value breakdown that might be verbose; some of this could be streamlined without losing clarity, but overall it remains efficient and informative.

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

Completeness5/5

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 output) and no annotations or output schema, the description is highly complete. It covers purpose, parameters, return values with examples, and error handling, providing all necessary context for an AI agent to understand and use the tool effectively without relying on structured fields.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate. It provides detailed parameter semantics: 'email (str): The email address to validate.' This adds clear meaning beyond the bare schema, explaining the parameter's purpose and type, which is essential given the low schema coverage.

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: 'Validates an email address using an external email validation API of abstractapi.' It specifies the verb ('validates'), resource ('email address'), and method ('external email validation API'), distinguishing it from sibling tools like 'check_email_reputation' which likely focuses on reputation rather than validation, and 'validate_phone' which handles a different resource type.

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 email validation but does not explicitly state when to use this tool versus alternatives like 'check_email_reputation'. It mentions checking 'validity, deliverability, and other attributes', which suggests use cases, but lacks explicit guidance on when to choose this over siblings or when not to use it (e.g., for simple format checks only).

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. Dates show when Glama detected each change.

  1. 3 tool updates
    • First observedcheck_email_reputation
    • First observedvalidate_phone
    • First observedverify_email

TDQS

A3.8/5.0
Disambiguation2/5

The tools have significant overlap and unclear boundaries. Both check_email_reputation and verify_email perform email validation with substantial functional overlap, making it difficult for an agent to choose between them. The phone validation tool is distinct, but the email tools appear to do similar things with different emphasis.

Naming Consistency3/5

The naming follows a mixed pattern. Two tools use verb_noun format (check_email_reputation, verify_email) while one uses verb_noun format but with different verb style (validate_phone). The naming is readable but lacks complete consistency in verb choice across the set.

Tool Count3/5

With only 3 tools, the server feels thin for an 'abstractapi-mcp-server' that presumably covers multiple Abstract API services. While the tools themselves are substantial, the count suggests limited coverage of what Abstract API likely offers, making the server feel under-scoped.

Completeness2/5

For an Abstract API server, there are significant gaps in coverage. The server only covers email and phone validation, missing other Abstract API services like IP geolocation, exchange rates, holidays, etc. Even within the covered domains, there's redundancy rather than comprehensive functionality.

Maintenance

ActivityInactive
ResponsivenessNo issues

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