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lakome-learn-n-grow

linkedin-profile-scraper

Server Quality Checklist

75%
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  • Latest release: v1.0.1

  • Disambiguation5/5

    Only one tool exists, so there is zero ambiguity between tools. The tool's purpose is clearly defined and distinct.

    Naming Consistency5/5

    The single tool follows a clear verb_noun pattern (extract_linkedin_profile), which is consistent and descriptive.

    Tool Count3/5

    A single tool is borderline for a server, but for a focused LinkedIn profile scraper it is not unreasonable. Still, the surface is minimal and offers no auxiliary capabilities.

    Completeness5/5

    The tool's description indicates it extracts comprehensive structured profile data from one or more URLs, which fully covers the server's stated purpose. No obvious gaps in the domain of LinkedIn profile scraping.

  • Average 3.7/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 4 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
  • 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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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?

    When annotations are absent, the description carries the full disclosure burden. It only says 'extracts' and 'using the Lakome API', but leaves out whether network access is required, whether authentication is needed, whether any state changes occur, and what 'intelligence' entails.

    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?

    A single sentence conveys the action, resource, input cardinality, and backend API. There is no fluff, and the key detail that this is a batch-style URL extraction is front-loaded.

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

    Completeness3/5

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

    The core purpose is clear, but the absence of an output schema and behavioral notes means the agent lacks information about the structure of the returned data, failure behavior, or what data is actually considered 'intelligence'. For such a simple tool, the bare-bones description is minimal but still has noticeable gaps.

    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 already documents the single parameter (profileUrls) with an example, giving 100% coverage. The description adds no new parameter-level information beyond restating the one-or-more constraint.

    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 uses a specific verb ('extract') and identifies the resource clearly: comprehensive structured profile data and intelligence from LinkedIn profile URLs via the Lakome API. It distinguishes the tool's scope immediately, including the ability to handle one or more URLs.

    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 usage context is explicit: call this tool when you need structured LinkedIn profile data from the given profile URLs. No exclusions or alternative tools exist in the provided context, so the clear scenario description 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.

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