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BrennerSpear

hunter-mcp

by BrennerSpear
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TypeScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: one discovers an email address from a name and domain, the other checks if an existing email is deliverable. No overlap or confusion.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: find_email_by_name and verify_email. The naming is predictable and readable.

    Tool Count4/5

    Only two tools, which is slightly below the typical 3-15 range, but they cover the core functions of an email lookup/verification service and each tool is essential.

    Completeness5/5

    The stated domain is email discovery and validation, and the two tools provide a complete workflow: find an email, then verify it. No obvious gaps for the intended use case.

  • Average 3.6/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only states that the tool 'finds the most likely email address', without explaining how it works, what it returns, potential failure modes, or whether any external lookups or side effects occur. This is insufficient for a tool with no annotation context.

    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 sentence that is clear and free of redundant information. It front-loads the core action and resource 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?

    There is no output schema, so the description should clarify what the tool returns (e.g., an email address string, multiple options, confidence score). It does not, nor does it address edge cases such as 'no email found'. Given the simple tool and lack of annotations, this is a notable incompleteness.

    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 input schema already describes all three parameters (domain, first_name, last_name) with 100% coverage. The description adds minimal value by linking them to 'a person at a company domain', but this is already evident from the schema. Per the rubric, baseline 3 is appropriate when schema covers parameters well.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose: finding the most likely email address for a person at a company domain. The verb 'find' combined with the resource 'email address' is specific and distinguishes it from the sibling tool 'verify_email', which focuses on verification rather than discovery.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies that the tool should be used when you need to discover an email address for a known person and company domain. However, it does not explicitly mention when to use it versus the sibling tool 'verify_email', nor does it provide any exclusions or prerequisites.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description carries the transparency burden. It conveys that the tool performs a read-only-style check, but does not disclose details such as return format, potential side effects, or error behavior. The core behavior is stated, but without deeper context.

    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 sentence with no wasted words. It is front-loaded with the action and clearly conveys the purpose.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    The tool has a single parameter, no annotations, and no output schema. The description adequately conveys the operation, but since no output schema exists, an explicit statement of the return value (e.g., boolean) would enhance completeness. However, 'verify if' implies a boolean result, making this sufficient for a low-complexity tool.

    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?

    Schema coverage is 100%, and the parameter 'email' already has a clear description. The tool description does not add extra meaning beyond the schema, which is acceptable given the high schema coverage.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states a specific verb ('verify') and resource ('email address'), adding the scope 'valid and deliverable'. This distinguishes it from the sibling tool find_email_by_name, which searches for emails by name.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies usage when an email needs validity/deliverability checking, but it does not explicitly state when to use this tool over alternatives like find_email_by_name. No exclusions or alternative conditions are provided.

    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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