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hunter-io

Hunter MCP Server

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by hunter-io

domain_search

Find key email contacts at any company domain to connect with decision-makers and build professional relationships.

Instructions

Return the top 10 emails on a given domain.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYes

Implementation Reference

  • main.py:8-12 (handler)
    The domain_search tool handler: an async function decorated as an MCP tool that takes a domain string, queries the Hunter API client for domain-search endpoint, and returns the response (top 10 emails).
    @mcp.tool(description="Return the top 10 emails on a given domain.")
    async def domain_search(domain: str) -> str:
        async with HunterAPIClient() as client:
            response = await client.get("domain-search", {"domain": domain})
            return response
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 mentions returning 'top 10 emails' but doesn't explain criteria for 'top' (e.g., by relevance, recency), potential rate limits, authentication needs, or data sources. 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.

Conciseness5/5

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

The description is a single, direct sentence with zero waste, front-loading the core functionality. It efficiently communicates the tool's purpose without unnecessary details, making it highly concise and well-structured.

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 parameter coverage, the description is incomplete. It doesn't address behavioral aspects like how emails are ranked, data freshness, or error handling, which are crucial for a search tool with no structured support.

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 meaning beyond the input schema, which has 0% description coverage. It implies the 'domain' parameter is used to search for emails, but doesn't specify format (e.g., 'example.com'), validation, or examples. With low schema coverage, this provides some compensation but is insufficient for full clarity.

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 action ('Return') and resource ('top 10 emails on a given domain'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'email_finder' or 'email_verifier', which may have overlapping functionality, preventing a perfect score.

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 such as 'email_finder' or 'enrich_email', nor does it mention any prerequisites or exclusions. It simply states what the tool does without contextual usage information.

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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