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Hunter MCP Server

Official
by hunter-io
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Python
Remote

email_verifier

Verify email address validity to ensure deliverability and reduce bounce rates for business communications.

Instructions

Return the validity of a given email address.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailYes

Implementation Reference

  • main.py:14-18 (handler)
    The handler function for the 'email_verifier' tool, decorated with @mcp.tool which also serves as registration. It verifies the given email address using the HunterAPIClient.
    @mcp.tool(description="Return the validity of a given email address.")
async def email_verifier(email: str) -> str:
    async with HunterAPIClient() as client:
        response = await client.get("email-verifier", {"email": email})
        return response
  • main.py:14-14 (registration)
    Registration of the 'email_verifier' tool via the FastMCP decorator.
    @mcp.tool(description="Return the validity of a given email address.")

Tool Definition Quality

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 returns validity but doesn't explain what that entails (e.g., format of return, error handling, rate limits, or authentication needs). This leaves significant gaps in understanding how the tool behaves beyond its basic function.

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 function without any unnecessary words. It is appropriately sized and front-loaded, making it easy to parse quickly.

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, output schema, and low schema description coverage, the description is incomplete. It doesn't address behavioral aspects like return format, error cases, or usage context, which are crucial for a tool that performs validation. This leaves the agent with insufficient information to use the tool 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 schema description coverage is 0%, so the description must compensate for the lack of parameter documentation. It implies the parameter is an email address but doesn't add any meaning beyond what the schema's title ('Email') and type ('string') provide. With only one parameter, the baseline is higher, but the description doesn't elaborate on format or constraints.

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 with a specific verb ('Return') and resource ('validity of a given email address'), making it easy to understand what it does. However, it doesn't explicitly distinguish this tool from potential sibling tools like 'enrich_email' or 'email_finder', which might have overlapping or related functionality.

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 any context, prerequisites, or exclusions, leaving the agent to infer usage based on the tool name alone, which is insufficient for optimal tool selection.

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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