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Icypeas MCP Server

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Icypeas MCP 服务器

这是一个模型上下文协议 (MCP) 服务器，它与 Icypeas API 集成以提供工作电子邮件查找功能。

特征

  • 使用姓名和公司信息查找工作电子邮件

Related MCP server: Hatch MCP Server

设置

本地设置

  1. 克隆此存储库

  2. 安装依赖项：

    npm install

  3. 根据.env.example创建一个.env文件并添加您的 Icypeas API 密钥：

    ICYPEAS_API_KEY=your_api_key_here

  4. 构建服务器：

    npm run build

  5. 启动服务器：

    npm start

Docker 设置

  1. 克隆此存储库

  2. 使用你的 Icypeas API 密钥创建一个.env文件

  3. 使用 Docker Compose 构建并运行：

    docker-compose up -d

MCP 配置

要将此服务器与 MCP 客户端一起使用，请将以下配置添加到您的 MCP 设置文件中：

{
  "mcpServers": {
    "icypeas": {
      "command": "node",
      "args": ["path/to/icypeas/dist/index.js"],
      "env": {
        "ICYPEAS_API_KEY": "your_api_key_here"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

可用工具

  • icypeas_find_work_email ：使用姓名和公司信息查找工作电子邮件

执照

国际学习中心

Available Tools

1 tool
icypeas_find_work_emailC

Find a work email using name and company information.

ParametersJSON Schema
NameRequiredDescriptionDefault
domainOrCompanyYesThe domain or company name
firstnameYesThe first name of the person
lastnameYesThe last name of the person

TDQS

C2.9/5.0
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 mentions 'Find a work email' but doesn't explain how it works (e.g., search method, data sources), success rates, error handling, or privacy considerations. This leaves significant gaps in understanding the tool's behavior and reliability.

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 directly states the tool's purpose without unnecessary words. It is front-loaded and easy to parse, making it highly concise and well-structured for quick understanding.

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 annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., email format, confidence scores) or behavioral aspects like rate limits or data sources. For a tool that likely involves external data lookups, more context is needed to use it effectively.

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 has 100% description coverage, clearly documenting all three required parameters. The description adds minimal value beyond the schema by implying the parameters are used together to find an email, but it doesn't provide additional context like format examples or usage tips. 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.

Purpose4/5

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

The description clearly states the tool's purpose: 'Find a work email using name and company information.' It specifies the verb ('Find'), resource ('work email'), and required inputs, making it easy to understand what the tool does. However, since there are no sibling tools, it cannot distinguish from alternatives, 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.

Usage Guidelines2/5

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, prerequisites, or limitations. It simply states what the tool does without context about its applicability, such as when it's most effective or any constraints on the data it can handle.

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. 1 tool updatev1.0.0
    • First observedicypeas_find_work_email
Deploy Server
Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

TDQS

B3.1/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly distinct as it is the only one available.

Naming Consistency5/5

The single tool name follows a consistent snake_case pattern with a clear verb_noun structure (find_work_email). Since there is only one tool, naming consistency is inherently perfect.

Tool Count2/5

One tool is too few for a server's apparent scope, as it suggests a very limited functionality that may not support typical agent workflows. This feels thin and incomplete for a general-purpose server.

Completeness2/5

The server's domain appears to be email or contact lookup, but with only one tool, there are significant gaps. For example, there are no tools for verifying emails, updating contact information, or handling other related operations, which will likely cause agent failures in broader tasks.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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