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trykittai-mcp-server

Name: trykittai-mcp-server
Author: avivshafir

by avivshafir

Overview Schema Source Related Servers Discussions Admin

Python

Remote

TryKitt.ai mcp 服务器

一个 FastMCP（模型上下文协议）服务器，使用TryKitt.ai API 提供电子邮件验证和查找功能。该服务器使 AI 助手能够以高精度和低退回率查找和验证 B2B 电子邮件地址。

特征

电子邮件验证：使用高级 SMTP 和 catchall 验证来验证电子邮件地址
电子邮件查找：使用姓名和公司域名查找个人的电子邮件地址
工作管理：跟踪和监控电子邮件验证/寻找工作
实时处理：立即获得电子邮件操作的结果
高精度：利用 TryKitt.ai 的先进验证算法，跳出率低于 0.1%

Related MCP server: ones-wiki-mcp-server

安装

克隆此存储库：

git clone https://github.com/avivshafir/trykittai-mcp-server
cd trykittai-mcp-server

使用 uv 初始化一个新的 Python 环境：

# Initialize a new uv project (if starting fresh)
uv init

# Or create a virtual environment
uv venv

# Activate the virtual environment
source .venv/bin/activate  # On macOS/Linux

使用 uv 安装依赖项：

# Using uv (recommended)
uv sync

设置

获取您的 TryKitt.ai API 密钥：
- 访问TryKitt.ai
- 注册账户
- 导航到您的 API 设置以获取您的 API 密钥
将您的 API 密钥设置为环境变量：

export TRYKITT_API_KEY="your_api_key_here"

或者在项目根目录中创建一个.env文件：

TRYKITT_API_KEY=your_api_key_here

用法

运行服务器

启动 FastMCP 服务器：

python server.py

服务器将启动并可用于 MCP 连接。

添加至 MCP 客户端

要将此服务器与 MCP 兼容客户端一起使用，您需要配置客户端以连接到此服务器。

克劳德桌面

将以下配置添加到您的 Claude Desktop 配置文件：

macOS ： ~/Library/Application Support/Claude/claude_desktop_config.json Windows ： %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "trykittai": {
      "command": "python",
      "args": ["/path/to/your/trykittai-mcp-server/server.py"],
      "env": {
        "TRYKITT_API_KEY": "your_api_key_here"
      }
    }
  }
}

其他 MCP 客户端

对于其他与 MCP 兼容的客户端，请将其配置为连接到：

命令： python
参数： ["/path/to/your/trykittai-mcp-server/server.py"]
环境变量： TRYKITT_API_KEY=your_api_key_here

与紫外线一起使用

如果您使用 uv，您还可以使用以下命令运行服务器：

{
  "mcpServers": {
    "trykittai": {
      "command": "uv",
      "args": ["run", "python", "server.py"],
      "cwd": "/path/to/your/trykittai-mcp-server",
      "env": {
        "TRYKITT_API_KEY": "your_api_key_here"
      }
    }
  }
}

注意：将/path/to/your/trykittai-mcp-server替换为您的项目目录的实际绝对路径，并将your_api_key_here为您的实际 TryKitt.ai API 密钥。

可用工具

1. 电子邮件验证（ `verify_email_send` ）

验证电子邮件地址是否有效且可传送。

参数：

email （必填）：需要验证的电子邮件地址
custom_data （可选）：与请求关联的自定义数据

例子：

result = await verify_email_send("john.doe@example.com")

2. 电子邮件查找（ `find_email` ）

根据某人的姓名和公司域名查找其电子邮件地址。

参数：

full_name （必填）：此人的全名
domain （必填）：公司域名或网站
linkedin_url （可选）：LinkedIn 个人资料 URL，以提高准确性
custom_data （可选）：与请求关联的自定义数据

例子：

result = await find_email(
    full_name="John Doe",
    domain="example.com",
    linkedin_url="https://linkedin.com/in/johndoe"
)

3. 作业状态（ `get_job_status` ）

检查先前提交的作业的状态。

参数：

job_id （必需）：要检查的作业的 ID

例子：

result = await get_job_status("job_123456")

4.列出作业（ `list_jobs` ）

列出所有作业（注意：此端点的可用性可能有限）。

例子：

result = await list_jobs()

API 响应格式

电子邮件验证成功

{
  "id": "job_123456",
  "status": "completed",
  "result": {
    "email": "john.doe@example.com",
    "valid": true,
    "deliverable": true,
    "confidence": 0.95,
    "verification_type": "smtp_catchall"
  }
}

成功查找电子邮件

{
  "id": "job_789012",
  "status": "completed",
  "result": {
    "email": "john.doe@example.com",
    "confidence": 0.88,
    "sources": ["pattern_matching", "web_scraping"]
  }
}

错误处理

服务器处理各种错误情况：

无效的 API 密钥
速率限制
网络超时
电子邮件格式无效
域名验证失败

常见错误响应：

{
  "error": "Invalid API key",
  "code": 401
}

配置

环境变量

TRYKITT_API_KEY ：您的 TryKitt.ai API 密钥（必需）

SSL 配置

该服务器已配置为与 TryKitt.ai 的 API 端点配合使用。SSL 验证目前已禁用，以实现兼容性。

发展

项目结构

trykittai-mcp-server/
├── server.py          # Main FastMCP server implementation
├── pyproject.toml     # Project dependencies and configuration
├── uv.lock           # Dependency lock file
├── README.md         # This file
├── LICENSE           # MIT License
└── .venv/            # Virtual environment

依赖项

fastmcp ：用于构建 MCP 服务器的 FastMCP 框架
httpx ：用于 API 请求的异步 HTTP 客户端
pydantic ：数据验证和设置管理

关于TryKitt.ai

TryKitt.ai 是一种先进的电子邮件验证和查找服务，它：

为个人用户提供无限制的免费电子邮件验证
通过高级验证实现<0.1％的跳出率
比其他解决方案快 2-5 倍
使用企业身份服务器进行全面验证
检测作业变化并针对真实系统进行验证

了解更多信息，请访问 https://trykitt.ai/

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。

贡献

分叉存储库
创建功能分支
进行更改
如果适用，添加测试
提交拉取请求

支持

对于相关问题：

此 MCP 服务器：在此存储库中打开一个问题
TryKitt.ai API：联系 TryKitt.ai 支持
FastMCP 框架：查看 FastMCP 文档

变更日志

v1.0.0

首次发布具有电子邮件验证和查找功能
工作状态跟踪
实时处理支持
FastMCP 集成

Available Tools

4 tools

find_emailC

Find an email address for a person.

Args:
    full_name: The full name of the person
    domain: The company domain or website
    linkedin_url: Optional LinkedIn profile URL
    custom_data: Optional custom data to associate with the request

ParametersJSON Schema

Name	Required	Description	Default
`full_name`	Yes
`domain`	Yes
`linkedin_url`	No
`custom_data`	No

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 states the tool 'finds' an email address, implying a read-only operation, but does not specify accuracy, data sources, rate limits, or authentication needs. For a tool with no annotations and potential privacy implications, this is a significant gap in transparency.

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 concise and front-loaded with the purpose, followed by parameter details. It uses a clear structure with bullet points for args. However, the parameter explanations are very brief and could be more informative, slightly reducing 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 the complexity of finding email addresses, no annotations, no output schema, and low parameter coverage, the description is incomplete. It lacks details on return values, error handling, data sources, and accuracy, which are crucial for effective tool use. The description does not adequately compensate for the missing structured data.

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 description adds minimal semantics beyond the input schema. It lists parameters with brief explanations (e.g., 'full_name: The full name of the person'), but with 0% schema description coverage, it does not fully compensate. The explanations are basic and do not provide format details or usage examples, leaving gaps for the required parameters.

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 an email address for a person.' It specifies the verb ('find') and resource ('email address'), but does not distinguish it from sibling tools like 'verify_email_send', which might have overlapping functionality. The purpose is specific but lacks sibling differentiation.

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. It does not mention sibling tools like 'verify_email_send' or specify contexts where this tool is preferred. Usage is implied only through the parameter descriptions, but no explicit when/when-not instructions are given.

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

get_job_statusB

Get the status of a job.

Args:
    job_id: The ID of the job to check

ParametersJSON Schema

Name	Required	Description	Default
`job_id`	Yes

TDQS

B3.1/5.0

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 states the tool 'Get[s] the status of a job,' implying a read-only operation, but doesn't clarify aspects like whether it requires authentication, has rate limits, returns specific status formats (e.g., pending, completed), or handles errors. This leaves significant gaps for an agent to understand how to use it effectively.

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 highly concise and well-structured. It starts with a clear purpose statement, followed by a brief 'Args' section that lists the parameter with a simple explanation. There's no unnecessary information, and every sentence serves a functional role in guiding usage.

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 cover behavioral aspects like authentication needs, error handling, or what the status output looks like (e.g., string values, timestamps). For a tool that likely returns critical operational data, this leaves the agent without enough context to use it reliably in complex scenarios.

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 description adds meaningful context for the single parameter: 'job_id: The ID of the job to check.' This clarifies that 'job_id' is an identifier used to retrieve status, which is helpful since schema description coverage is 0% (the schema only provides a title and type without explanation). With one parameter, the baseline is 4, and the description compensates well by explaining its purpose.

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: 'Get the status of a job.' It uses a specific verb ('Get') and resource ('status of a job'), making the function unambiguous. However, it doesn't differentiate from sibling tools like 'list_jobs', which might provide a broader overview rather than specific status checks.

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. It doesn't mention sibling tools like 'list_jobs' for listing multiple jobs or other tools for related operations. There's no context about prerequisites, such as needing a job ID from another operation, or when this tool is appropriate.

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

list_jobsD

List jobs

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

D1.8/5.0

Behavior1/5

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 but offers none. 'List jobs' doesn't indicate whether this is a read-only operation, whether it requires authentication, what format results are returned in, if there are rate limits, or any other behavioral characteristics. The description fails to provide any operational context beyond the basic action.

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

Conciseness2/5

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

While technically concise with just two words, this description represents under-specification rather than effective brevity. The single phrase 'List jobs' fails to provide necessary context that would help an agent understand when and how to use this tool. Conciseness should not come at the expense of clarity and completeness.

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

Completeness1/5

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

For a tool with no annotations and no output schema, the description 'List jobs' is completely inadequate. It doesn't explain what constitutes a 'job' in this context, what information is returned, whether results are paginated, or any other operational details. The description fails to provide the minimal context needed for effective tool invocation.

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 tool has zero parameters, and the input schema has 100% description coverage (though empty). With no parameters to document, the description doesn't need to compensate for schema gaps. The baseline for zero-parameter tools is 4, as there's no parameter semantics burden on the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

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

The description 'List jobs' is a tautology that merely restates the tool name without adding meaningful context. It specifies the verb ('list') and resource ('jobs'), but provides no differentiation from sibling tools like 'get_job_status' or additional scope information. This minimal description fails to clarify what type of jobs are being listed or under what conditions.

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

Usage Guidelines1/5

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

The description provides absolutely no guidance on when to use this tool versus alternatives like 'get_job_status' or other sibling tools. There's no mention of appropriate contexts, prerequisites, or exclusions. Users must infer usage patterns from the tool name alone, which is insufficient for effective tool selection.

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

verify_email_sendC

verify an email using trykitt.

Args:
    email: The email address to verify
    custom_data: Optional custom data to associate with the request

ParametersJSON Schema

Name	Required	Description	Default
`email`	Yes
`custom_data`	No

TDQS

C2.6/5.0

Behavior2/5

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 mentions 'verify an email using trykitt' but fails to explain key traits like whether this is a read-only or mutative operation, what the expected outcome is (e.g., sends an email, returns a status), or any rate limits or authentication needs. This leaves significant gaps in understanding the tool's behavior.

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 brief and front-loaded with the main purpose, followed by parameter details in a structured 'Args:' section. It avoids unnecessary elaboration, but the lack of context and behavioral details means it could be more informative without sacrificing conciseness.

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 tool's complexity (2 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what 'verify' means in practice, what happens after invocation (e.g., sends an email, returns a job ID), or how it relates to sibling tools, leaving the agent with insufficient context for effective use.

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 description lists parameters ('email' and 'custom_data') and notes that 'custom_data' is optional, adding basic semantics beyond the input schema. However, with 0% schema description coverage, it doesn't fully compensate by explaining parameter formats (e.g., email validation rules, custom_data structure), leaving the agent with incomplete information for proper usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

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

The description states 'verify an email using trykitt' which provides a basic verb+resource combination, but it's vague about what verification entails (e.g., sending a verification email, checking validity). It doesn't distinguish from siblings like 'find_email' or 'get_job_status', leaving ambiguity about the specific action.

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?

No guidance is provided on when to use this tool versus alternatives like 'find_email' or 'get_job_status'. The description lacks context about prerequisites, such as whether this initiates a verification process or checks an existing one, leaving the agent without clear usage instructions.

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.

4 tool updatesAug 15, 2025
- First observedfind_email
- First observedget_job_status
- First observedlist_jobs
- First observedverify_email_send

Deploy Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

C2.9/5.0

Scored 2026-04-18 10:00 across 4 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: find_email locates email addresses, get_job_status checks job status, list_jobs enumerates jobs, and verify_email_send validates emails. The descriptions clearly differentiate their functions, eliminating any potential for agent misselection.

Naming Consistency4/5

The naming follows a consistent verb_noun pattern (find_email, get_job_status, list_jobs, verify_email_send), with all tools using snake_case. The minor deviation is 'verify_email_send' which includes an extra verb 'send', but overall the pattern is predictable and readable.

Tool Count5/5

With 4 tools, this server is well-scoped for its purpose of email and job management. Each tool earns its place by covering distinct aspects: email discovery, job tracking, and email verification, without being overly sparse or bloated.

Completeness3/5

The tool surface covers core operations like finding and verifying emails, and managing jobs, but has notable gaps. For example, there are no tools to create or delete jobs, or to handle email sending beyond verification, which could limit agent workflows in this domain.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

Reddit Discussion

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Appeared in Searches

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TryKitt.ai mcp 服务器

特征

安装

设置

用法

运行服务器

添加至 MCP 客户端

克劳德桌面

其他 MCP 客户端

与紫外线一起使用

可用工具

1. 电子邮件验证（ verify_email_send ）

2. 电子邮件查找（ find_email ）

3. 作业状态（ get_job_status ）

4.列出作业（ list_jobs ）

API 响应格式

电子邮件验证成功

成功查找电子邮件

错误处理

配置

环境变量

SSL 配置

发展

项目结构

依赖项

关于TryKitt.ai

执照

贡献

支持

变更日志

v1.0.0

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Appeared in Searches

1. 电子邮件验证（ `verify_email_send` ）

2. 电子邮件查找（ `find_email` ）

3. 作业状态（ `get_job_status` ）

4.列出作业（ `list_jobs` ）