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mambalabsdev

mcp-people-finder

by mambalabsdev
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JavaScript
Remote

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools. The tool's purpose is clearly described and self-contained.

    Naming Consistency5/5

    The single tool name follows the verb_noun pattern (find_people_and_emails), which is clear and descriptive. With only one tool, consistency is inherently maintained.

    Tool Count3/5

    The server has exactly one tool, which feels thin for a general-purpose people finder service. While the tool is comprehensive, a single tool may not cover all potential use cases or allow for modularity.

    Completeness5/5

    The tool covers the full cycle of finding people and emails, including company identification, filtering, batch processing, and email verification. It is read-only, but no obvious gaps exist for its stated purpose.

  • Average 4.5/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 3 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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  "$schema": "https://glama.ai/mcp/schemas/server.json",
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  ]
}

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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

  • Behavior5/5

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

    The description discloses many behavioral traits beyond annotations: the four-layer cascade, no LinkedIn scraping, email provider waterfall, catch-all escalation, and the fact that provider keys are used directly with no markup. It also confirms the read-only nature, aligning with readOnlyHint and destructiveHint. This rich context adds significant value over the annotations.

    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 dense but well-structured, starting with the core purpose and then detailing pipeline behavior, constraints, and billing. Every sentence carries useful information; however, the length is considerable. It remains appropriately sized for a complex 22-parameter tool, and the front-loading of the core function is effective.

    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 description covers identification, filtering, pipeline, costs, provider keys, and read-only behavior. Since there is no output schema, it does not explicitly specify the return structure beyond 'every output row echoes its source domain' and mentions a summary for shortfalls. This is a minor gap; otherwise the context is comprehensive for the tool's complexity.

    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 input schema already describes all 22 parameters at 100% coverage, so the baseline is 3. The description adds high-level semantic context, such as targetCount acting as a cost cap, the precedence of domains over domain, and the overall identification modes. This goes slightly beyond the schema's per-parameter descriptions, earning a 4.

    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 opens with a clear verb and resource: 'Find people at a company and optionally discover and verify their business email.' It then enumerates identification methods (domain, company name, LinkedIn URL, batch domains) and filtering dimensions, which fully specifies the tool's purpose. There are no sibling tools to distinguish from, and the description is unambiguous.

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

    Usage Guidelines4/5

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

    The description provides clear usage context: prerequisites (APIFY_TOKEN), cost implications (targetCount hard cap, provider billing), and consequences of missing keys (no Serper means lower coverage). It doesn't explicitly state 'use this when...' but the context is sufficient given no siblings. It lacks an explicit 'when not to use' section, which prevents a 5.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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