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mambalabsdev

mcp-domain-to-linkedin-url-resolver

by mambalabsdev
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.5

  • Disambiguation5/5

    With only one tool, there is no risk of ambiguity between tools. The single tool has a distinct and clear purpose.

    Naming Consistency5/5

    The single tool name 'resolve_linkedin_url' follows a clear verb_noun pattern, consistent with best practices.

    Tool Count3/5

    One tool is at the lower end of the range. While it serves a specific resolver function, the surface is minimal and might feel thin for broader use cases.

    Completeness5/5

    For the stated purpose of resolving a domain or name to a LinkedIn URL with firmographics, the single tool fully covers the intended operation with no obvious gaps.

  • 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
    • 27 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.

  • This repository includes a glama.json configuration file.

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  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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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

  • Behavior4/5

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

    Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds context about requiring APIFY_TOKEN and consuming credits, which is valuable beyond annotations. No contradictions.

    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 two sentences long, efficiently conveying purpose, inputs, outputs, and constraints. No filler or redundant information.

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

    Completeness5/5

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

    Despite lacking an output schema, the description fully specifies the return content (firmographics, confidence score, social links) and format (flat, Clay-ready JSON row). All relevant aspects are covered.

    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?

    Schema coverage is 100% with descriptions for both parameters. The description further clarifies that company_domain should be 'bare' without protocol or trailing slash, providing an example. This adds meaningful detail beyond the schema.

    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 resolves a company domain or name to its LinkedIn URL and lists the returned data (confidence score, firmographics, social links). It provides a specific verb ('resolve') and resource ('LinkedIn URL'), making its purpose 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?

    It explicitly states the input requirement ('provide at least one of company_domain or company_name') and notes prerequisites (APIFY_TOKEN, credit consumption). While it does not mention alternatives, no siblings exist, so this is sufficient.

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

GitHub Badge

Glama performs regular codebase and documentation scans to:

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

Our badge communicates server capabilities, safety, and installation instructions.

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