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find_email

Find email addresses for individuals by providing their full name and company domain, optionally using LinkedIn profiles for enhanced accuracy.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
full_nameYes
domainYes
linkedin_urlNo
custom_dataNo

Implementation Reference

  • server.py:41-64 (handler)
    The main handler function for the 'find_email' tool. It is registered via the @mcp.tool() decorator. The function takes full_name, domain, optional linkedin_url and custom_data, builds a payload, sends a POST request to the TryKitt API's /job/find_email endpoint, and returns the JSON response. This implements the core tool logic.
    @mcp.tool()
    async def find_email(
        full_name: str,
        domain: str,
        linkedin_url: Optional[str] = None,
        custom_data: Optional[str] = None,
    ) -> Dict:
        """
        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
        """
        payload = {"fullName": full_name, "domain": domain, "realtime": True}
        if linkedin_url:
            payload["linkedinStandardProfileURL"] = linkedin_url
        if custom_data:
            payload["customData"] = custom_data
    
        response = await http_client.post("/job/find_email", json=payload)
        return response.json()
  • Input schema defined by function parameters with type annotations (str, Optional[str]) and comprehensive docstring describing args and purpose. Output is Dict from API response.
    async def find_email(
        full_name: str,
        domain: str,
        linkedin_url: Optional[str] = None,
        custom_data: Optional[str] = None,
    ) -> Dict:
        """
        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
        """
  • server.py:41-41 (registration)
    The @mcp.tool() decorator registers the find_email function as an MCP tool in the FastMCP server.
    @mcp.tool()
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.

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