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BACH-AI-Tools

Whatsapp Number Validator3 MCP Server

by BACH-AI-Tools
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Python
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Whatsapp Number Validator3 MCP Server

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Related MCP server: Bulk WhatsApp Validator

简介

这是一个 MCP 服务器，用于访问 Whatsapp Number Validator3 API。

  • PyPI 包名: bach-whatsapp_number_validator3

  • 版本: 2.0.0

  • 传输协议: stdio

安装

从 PyPI 安装:

pip install bach-whatsapp_number_validator3

从源码安装:

pip install -e .

运行

方式 1: 使用 uvx（推荐，无需安装）

# 运行（uvx 会自动安装并运行）
uvx --from bach-whatsapp_number_validator3 bach_whatsapp_number_validator3

# 或指定版本
uvx --from bach-whatsapp_number_validator3@latest bach_whatsapp_number_validator3

方式 2: 直接运行（开发模式）

python server.py

方式 3: 安装后作为命令运行

# 安装
pip install bach-whatsapp_number_validator3

# 运行（命令名使用下划线）
bach_whatsapp_number_validator3

配置

API 认证

此 API 需要认证。请设置环境变量:

export API_KEY="your_api_key_here"

环境变量

变量名

说明

必需

API_KEY

API 密钥

是

在 Claude Desktop 中使用

编辑 Claude Desktop 配置文件 claude_desktop_config.json:

{
  "mcpServers": {
    "whatsapp_number_validator3": {
      "command": "uvx",
      "args": ["--from", "bach-whatsapp_number_validator3", "bach_whatsapp_number_validator3"],
      "env": {
        "API_KEY": "your_api_key_here"
      }
    }
  }
}

注意: 请将 E:\path\to\whatsapp_number_validator3\server.py 替换为实际的服务器文件路径。

可用工具

此服务器提供以下工具:

single_validation

Checks if a given number is registered on WhatsApp.

端点: POST /WhatsappNumberHasItWithToken

bulk_validation_10

Checks if the numbers in given list are registered on WhatsApp.

端点: POST /WhatsappNumberHasItBulkWithToken

技术栈

  • 传输协议: stdio

  • HTTP 客户端: httpx

开发

此服务器由 API-to-MCP 工具自动生成。

版本: 2.0.0

Available Tools

2 tools
bulk_validation_10A

Checks if the numbers in given list are registered on WhatsApp.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

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 carries the full burden. It conveys a read-only check, but does not disclose return format, potential limitations, or what 'registered' means. This is adequate but leaves gaps.

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 concise sentence that is front-loaded with the action. It earns its place, though the ambiguity about parameters slightly reduces efficiency.

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 and no annotations, the description should explain return values and how the list is provided. It fails to do so, making the tool incomplete for an agent to invoke correctly.

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

Parameters2/5

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

The input schema is empty, but the description references a 'given list', implying an input parameter that doesn't exist in the schema. This is misleading and adds confusion rather than clarity.

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 uses a specific verb ('Checks') and resource ('numbers in given list are registered on WhatsApp'), clearly indicating a bulk validation operation. It distinguishes from the sibling tool 'single_validation' by focusing on a list of numbers.

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 implies usage for validating multiple numbers, which contrasts with the single_validation sibling. However, it does not explicitly state when to use this tool over the alternative or mention any exclusions.

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

single_validationB

Checks if a given number is registered on WhatsApp.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the core action ('checks if registered') but does not disclose return format, side effects, authentication requirements, or any limitations, leaving the agent without critical context.

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, clear sentence that is front-loaded with the action and target. It contains no filler or unnecessary detail, making it efficiently concise.

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?

With no annotations, no output schema, and an empty input schema, the description is incomplete. It does not state what the tool returns, how to provide the number, or any other operational details, leaving the agent under-equipped to use the tool correctly.

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?

The input schema has zero parameters, and schema coverage is 100% by default. The description's mention of 'a given number' adds semantic meaning about the subject of validation, which exceeds the empty schema's information, though it does not clarify how the number is supplied.

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 uses the specific verb 'Checks' and clearly identifies the resource: whether a given number is registered on WhatsApp. This distinguishes it from the sibling tool 'bulk_validation_10' by explicitly handling a single number, despite not stating 'single' explicitly.

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?

There is no guidance on when to use this tool versus the alternative bulk_validation_10. The description does not mention any prerequisites, context, or scenarios that would favor this tool over its sibling.

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. 2 tool updatesv2.0.0
    • First observedbulk_validation_10
    • First observedsingle_validation
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TDQS

A3.6/5.0
Disambiguation5/5

The two tools are clearly distinct: one validates a single number, the other validates a list of numbers. An agent can easily choose based on the number of inputs.

Naming Consistency4/5

Both tools follow a similar pattern with 'validation' as the base noun, prefixed by 'single' and 'bulk_10'. The '10' in bulk_validation_10 is a minor oddity but does not break the overall consistency.

Tool Count3/5

With only 2 tools, the server feels minimal but not entirely inadequate for its narrow purpose. The count is borderline per the calibration, as 1-2 tools is considered thin.

Completeness4/5

The core use case of WhatsApp number validation is covered: single and bulk checks. The bulk tool is limited to 10 numbers, but agents can loop for larger batches, making it workable with minor gaps.

Maintenance

ActivityInactive
ResponsivenessNo issues

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