Which integrations are available for this server?

Enables configuration of API keys through .env files, allowing secure storage of TryKitt.ai credentials for the MCP server. Provides specific configuration paths for Claude Desktop on macOS systems, with detailed instructions for proper integration. Handles data validation and settings management for the server, ensuring proper formatting of requests to the TryKitt.ai API. Serves as the runtime environment for the MCP server, allowing execution of the server script for email verification and finding functionality.

How do I use trykittai-mcp-server?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@trykittai-mcp-server verify if john.doe@acme.com is a valid email address" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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trykittai-mcp-server

by avivshafir

Overview Schema Source Related Servers Discussions Admin

Python

Remote

TryKitt.ai mcp Server

A FastMCP (Model Context Protocol) server that provides email verification and finding capabilities using the TryKitt.ai API. This server enables AI assistants to find and verify B2B email addresses with high accuracy and low bounce rates.

Features

Email Verification: Verify email addresses with advanced SMTP and catchall verification
Email Finding: Find email addresses for individuals using their name and company domain
Job Management: Track and monitor email verification/finding jobs
Real-time Processing: Get immediate results for email operations
High Accuracy: Leverages TryKitt.ai's advanced verification algorithms with <0.1% bounce rate

Related MCP server: ones-wiki-mcp-server

Installation

Clone this repository:

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

Initialize a new Python environment with uv:

# 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

Install dependencies using uv:

# Using uv (recommended)
uv sync

Setup

Get your TryKitt.ai API key:
- Visit TryKitt.ai
- Sign up for an account
- Navigate to your API settings to get your API key
Set your API key as an environment variable:

export TRYKITT_API_KEY="your_api_key_here"

Or create a .env file in the project root:

TRYKITT_API_KEY=your_api_key_here

Usage

Running the Server

Start the FastMCP server:

python server.py

The server will start and be available for MCP connections.

Adding to MCP Clients

To use this server with MCP-compatible clients, you'll need to configure the client to connect to this server.

Claude Desktop

Add the following configuration to your Claude Desktop config file:

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"
      }
    }
  }
}

Other MCP Clients

For other MCP-compatible clients, configure them to connect to:

Command: python
Arguments: ["/path/to/your/trykittai-mcp-server/server.py"]
Environment Variables: TRYKITT_API_KEY=your_api_key_here

Using with uv

If you're using uv, you can also run the server with:

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

Note: Replace /path/to/your/trykittai-mcp-server with the actual absolute path to your project directory, and your_api_key_here with your actual TryKitt.ai API key.

Available Tools

1. Email Verification (`verify_email_send`)

Verify if an email address is valid and deliverable.

Parameters:

email (required): The email address to verify
custom_data (optional): Custom data to associate with the request

Example:

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

2. Email Finding (`find_email`)

Find an email address for a person based on their name and company domain.

Parameters:

full_name (required): The full name of the person
domain (required): The company domain or website
linkedin_url (optional): LinkedIn profile URL for better accuracy
custom_data (optional): Custom data to associate with the request

Example:

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

3. Job Status (`get_job_status`)

Check the status of a previously submitted job.

Parameters:

job_id (required): The ID of the job to check

Example:

result = await get_job_status("job_123456")

4. List Jobs (`list_jobs`)

List all jobs (Note: This endpoint may have limited availability).

Example:

result = await list_jobs()

API Response Format

Successful Email Verification

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

Successful Email Finding

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

Error Handling

The server handles various error scenarios:

Invalid API keys
Rate limiting
Network timeouts
Invalid email formats
Domain verification failures

Common error responses:

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

Configuration

Environment Variables

TRYKITT_API_KEY: Your TryKitt.ai API key (required)

SSL Configuration

The server is configured to work with TryKitt.ai's API endpoints. SSL verification is currently disabled for compatibility.

Development

Project Structure

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

Dependencies

fastmcp: FastMCP framework for building MCP servers
httpx: Async HTTP client for API requests
pydantic: Data validation and settings management

About TryKitt.ai

TryKitt.ai is an advanced email verification and finding service that:

Provides unlimited free email verification for individual users
Achieves <0.1% bounce rates through advanced verification
Works 2-5X faster than alternative solutions
Uses enterprise identity servers for catchall verification
Detects job changes and validates against real systems

Learn more at https://trykitt.ai/

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request

Support

For issues related to:

This MCP server: Open an issue in this repository
TryKitt.ai API: Contact TryKitt.ai support
FastMCP framework: Check the FastMCP documentation

Changelog

v1.0.0

Initial release with email verification and finding capabilities
Job status tracking
Real-time processing support
FastMCP integration

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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TryKitt.ai mcp Server

Features

Installation

Setup

Usage

Running the Server

Adding to MCP Clients

Claude Desktop

Other MCP Clients

Using with uv

Available Tools

1. Email Verification (verify_email_send)

2. Email Finding (find_email)

3. Job Status (get_job_status)

4. List Jobs (list_jobs)

API Response Format

Successful Email Verification

Successful Email Finding

Error Handling

Configuration

Environment Variables

SSL Configuration

Development

Project Structure

Dependencies

About TryKitt.ai

License

Contributing

Support

Changelog

v1.0.0

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Appeared in Searches

1. Email Verification (`verify_email_send`)

2. Email Finding (`find_email`)

3. Job Status (`get_job_status`)

4. List Jobs (`list_jobs`)