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Phicks-debug

linkedin-web-scrapper-mcp-server

by Phicks-debug
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one discovers profiles via search, the other extracts detailed data from a specific profile URL. There is no overlap in functionality.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern using 'search-linkedin-people' and 'scrape-linkedin-profile', maintaining the same structure and style.

    Tool Count3/5

    With only two tools, the server is minimal but each serves a necessary step in the LinkedIn people-scraping workflow. The count feels slightly thin, yet it aligns with the focused purpose of the server.

    Completeness4/5

    The tools form a complete flow: search for profiles and then scrape any selected profile. Minor gaps exist (e.g., no company or job search), but for a people-focused scraper, the core lifecycle is covered.

  • Average 3.4/5 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

  • 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",
  "maintainers": [
    "your-github-username"
  ]
}

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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?

    With no annotations, the description carries the full burden of behavioral disclosure. It mentions 'web scraping' and the return fields, but lacks important context such as whether authentication is required, rate limits, pagination, or potential failures. The absence of such details leaves the agent with significant uncertainty.

    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 immediately meaningful and front-loaded. Every word contributes to the understanding of the tool's function and output, with no waste.

    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?

    For a search tool with no output schema and no annotations, the description is too brief. It omits critical context such as the expected number of results, whether results are paginated, and any limitations of web scraping. The information provided is not enough for an agent to confidently rely on this 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?

    The input schema provides 100% coverage of all three parameters with descriptions. The tool description adds no additional parameter semantics beyond what the schema already states, so the baseline of 3 is appropriate.

    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: 'Search for LinkedIn profiles using web scraping.' The verb 'search' and resource 'LinkedIn profiles' are specific, and the method 'web scraping' sets it apart from the sibling tool 'scrape-linkedin-profile', which implies a different action on a specific profile.

    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?

    No guidance is provided on when to use this tool versus the sibling 'scrape-linkedin-profile'. While the name implies searching versus scraping, there is no explicit mention of alternatives, prerequisites, or cases where this tool should be avoided.

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

  • 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 describes what data is returned (experience, education, etc.) but does not disclose potential side effects, limitations (e.g., profile privacy, rate limits), or authentication requirements. For a scraping tool, such context is important but missing.

    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?

    Two concise sentences clearly state the action and the output categories. No filler or unnecessary repetition. The most important information is front-loaded.

    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?

    For a simple one-parameter tool with no output schema, the description adequately explains the return values by listing key sections. However, it lacks any caveats about scraping LinkedIn (e.g., profile must be public, potential blocks) which would be valuable for completeness, but the tool is simple enough that this is a minor gap.

    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 covers the single parameter (profileUrl) 100%, including an example. The description adds no additional meaning beyond what the schema already provides. Baseline of 3 is appropriate given full 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 the tool's specific action ('Scrape comprehensive data from a specific LinkedIn profile URL') and the resource. It distinguishes from the sibling tool 'search-linkedin-people' by emphasizing 'specific URL' versus search functionality.

    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: scrape when you have a specific LinkedIn profile URL. However, it does not explicitly state when not to use it or mention the alternative sibling tool (search-linkedin-people) for finding profiles. This is implied rather than explicitly guided.

    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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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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