de en es ja ko ru zh

trykittai-mcp-server

Name: trykittai-mcp-server
Author: avivshafir

by avivshafir

Overview Schema Source Related Servers Discussions Admin

Python

Remote

TryKitt.ai mcp サーバー

TryKitt.ai APIを使用したメール検証および検索機能を提供するFastMCP（Model Context Protocol）サーバー。このサーバーにより、AIアシスタントはB2Bメールアドレスを高精度かつ低いバウンス率で検出・検証できるようになります。

特徴

メール検証: 高度なSMTPおよびキャッチオール検証でメールアドレスを検証します
メール検索: 個人の名前と会社のドメインを使用して、個人のメールアドレスを検索します
求人管理: メールの検証/求人の検索を追跡および監視します
リアルタイム処理:電子メール操作の即時結果を取得
高精度: TryKitt.ai の高度な検証アルゴリズムを活用し、直帰率は 0.1% 未満

Related MCP server: ones-wiki-mcp-server

インストール

このリポジトリをクローンします:

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

uv を使用して新しい Python 環境を初期化します。

# 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

uv を使用して依存関係をインストールします。

# Using uv (recommended)
uv sync

設定

TryKitt.ai API キーを取得します:
- TryKitt.aiにアクセス
- アカウントを登録する
- API設定に移動してAPIキーを取得します
API キーを環境変数として設定します。

export TRYKITT_API_KEY="your_api_key_here"

または、プロジェクトルートに.envファイルを作成します。

TRYKITT_API_KEY=your_api_key_here

使用法

サーバーの実行

FastMCP サーバーを起動します。

python server.py

サーバーが起動し、MCP 接続が使用できるようになります。

MCPクライアントへの追加

このサーバーを MCP 互換クライアントで使用するには、このサーバーに接続するようにクライアントを構成する必要があります。

クロードデスクトップ

Claude Desktop 構成ファイルに次の構成を追加します。

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"
      }
    }
  }
}

その他のMCPクライアント

その他の MCP 互換クライアントの場合は、次のように接続するように設定します。

コマンド: python
引数: ["/path/to/your/trykittai-mcp-server/server.py"]
環境変数: TRYKITT_API_KEY=your_api_key_here

UVを使用する

uv を使用している場合は、次のコマンドでサーバーを実行することもできます。

{
  "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: /path/to/your/trykittai-mcp-serverプロジェクトディレクトリへの実際の絶対パスに置き換え、 your_api_key_hereを実際の TryKitt.ai API キーに置き換えます。

利用可能なツール

1. メール認証（ `verify_email_send` ）

電子メールアドレスが有効であり、配信可能かどうかを確認します。

パラメータ:

email （必須）: 確認するメールアドレス
custom_data (オプション): リクエストに関連付けるカスタムデータ

例：

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

2. メール検索（ `find_email` ）

名前と会社のドメインに基づいて、個人の電子メールアドレスを検索します。

パラメータ:

full_name （必須）: 人物のフルネーム
domain （必須）: 会社のドメインまたはウェブサイト
linkedin_url (オプション): 精度を上げるための LinkedIn プロフィールの URL
custom_data (オプション): リクエストに関連付けるカスタムデータ

例：

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

3. ジョブステータス（ `get_job_status` ）

以前に送信したジョブのステータスを確認します。

パラメータ:

job_id (必須): チェックするジョブのID

例：

result = await get_job_status("job_123456")

4. ジョブの一覧表示（ `list_jobs` ）

すべてのジョブを一覧表示します (注: このエンドポイントは利用が制限される場合があります)。

例：

result = await list_jobs()

APIレスポンスフォーマット

メール認証成功

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

メールの発見に成功

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

エラー処理

サーバーはさまざまなエラーシナリオを処理します。

無効なAPIキー
レート制限
ネットワークタイムアウト
無効なメール形式
ドメイン検証の失敗

一般的なエラー応答:

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

構成

環境変数

TRYKITT_API_KEY : TryKitt.ai APIキー（必須）

SSL設定

サーバーはTryKitt.aiのAPIエンドポイントと連携するように設定されています。互換性のため、SSL検証は現在無効になっています。

発達

プロジェクト構造

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

依存関係

fastmcp : MCP サーバーを構築するための FastMCP フレームワーク
httpx : APIリクエスト用の非同期HTTPクライアント
pydantic : データ検証と設定管理

TryKitt.aiについて

TryKitt.ai は、次のような高度な電子メール検証および検索サービスです。

個々のユーザーに無制限の無料メール認証を提供します
高度な検証により、直帰率0.1%未満を実現
他のソリューションよりも2～5倍高速に動作します
包括的な検証にエンタープライズ ID サーバーを使用する
ジョブの変更を検出し、実際のシステムに対して検証します

詳細については、https://trykitt.ai/をご覧ください。

ライセンス

このプロジェクトは MIT ライセンスに基づいてライセンスされています - 詳細についてはLICENSEファイルを参照してください。

貢献

リポジトリをフォークする
機能ブランチを作成する
変更を加える
該当する場合はテストを追加する
プルリクエストを送信する

サポート

以下に関連する問題について:

このMCPサーバー: このリポジトリで問題を開く
TryKitt.ai API: TryKitt.ai サポートにお問い合わせください
FastMCPフレームワーク: FastMCPのドキュメントを確認してください

変更履歴

バージョン1.0.0

メール認証と検索機能を備えた初期リリース
ジョブステータスの追跡
リアルタイム処理サポート
FastMCP統合

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

Related Servers

Related MCP Connectors

Second server!
com.fenetresurciel.verylongmcp
A non functional MCP server (yet)
Developer Tools
2
agentwork-mcp A
io.github.ferm-ai
Official MCP server for Agentwork — delegate tasks to AI agents with human-in-the-loop
Agent Orchestration Autonomous Agents App Automation
2
PortOAuth
io.port
Port.io mcp server
Developer Tools CI/CD & DevOps Cloud Platforms
214
mattest4-qyroac
ai.fugentic
Fugentic MCP server for mattest4-qyroac
Developer Tools
1

Related MCP Servers

mcp-server-cloudbrowser
Browser Automation Web Scraping
clpublic
F
license
Not graded
quality
D
maintenance
mcp-server-cloudbrowser
Updated a year ago (2025-04-26 06:28 UTC)
2
-
ones-wiki-mcp-server
Content Management Systems Developer Tools Documentation Access
brianxiadong
F
license
Not graded
quality
D
maintenance
ones-wiki-mcp-server
Updated a year ago (2025-05-29 03:59 UTC)
7
-
mcp-server-test
Vector Databases Blockchain
JussCubs
F
license
A
quality
D
maintenance
mcp-server-test
Updated a year ago (2025-04-04 22:28 UTC)
4
-
buildkite-mcp-server
CI/CD & DevOps Developer Tools Code Execution
buildkite
A
license
Not graded
quality
A
maintenance
Official MCP Server for Buildkite.
Updated a day ago (2026-09-11 20:39 UTC)
54
MIT

Appeared in Searches

Search for 'tc34jqkh7w'

TryKitt.ai mcp サーバー

特徴

インストール

設定

使用法

サーバーの実行

MCPクライアントへの追加

クロードデスクトップ

その他のMCPクライアント

UVを使用する

利用可能なツール

1. メール認証（ verify_email_send ）

2. メール検索（ find_email ）

3. ジョブステータス（ get_job_status ）

4. ジョブの一覧表示（ list_jobs ）

APIレスポンスフォーマット

メール認証成功

メールの発見に成功

エラー処理

構成

環境変数

SSL設定

発達

プロジェクト構造

依存関係

TryKitt.aiについて

ライセンス

貢献

サポート

変更履歴

バージョン1.0.0

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Appeared in Searches

1. メール認証（ `verify_email_send` ）

2. メール検索（ `find_email` ）

3. ジョブステータス（ `get_job_status` ）

4. ジョブの一覧表示（ `list_jobs` ）