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rcreech93

LinkedIn MCP

by rcreech93
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Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one extracts a full profile from a URL or username, while the other searches for profiles by name. There is no overlap or ambiguity in their intended use.

    Naming Consistency3/5

    The tool names do not follow a consistent verb_noun pattern. 'linkedin_profile' is a noun phrase, while 'profile_search' places the verb at the end. This is a minor inconsistency, but both names are descriptive and readable.

    Tool Count3/5

    With only 2 tools, the server feels slightly thin for a comprehensive LinkedIn integration. However, the two tools cover the core search-and-retrieve workflow, so the count is borderline appropriate for a focused profile-scraping server.

    Completeness4/5

    The tool set covers the main workflow of finding a profile via search and then retrieving its full details. Missing features like connections or posts are not critical for the apparent purpose of profile extraction, so the coverage is solid with only minor gaps.

  • Average 4.2/5 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.

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

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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

  • Behavior3/5

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

    The description reveals it uses web scraping with Selenium, which implies network/browser behavior, and includes an error example showing possible failure. However, it does not disclose potential issues like login requirements, rate limiting, or blocking, so transparency is moderate.

    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 well-structured with Args, Returns, and examples, making it scannable. The example output is lengthy but informative; while it could be trimmed, it mostly earns its place.

    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?

    With no annotations and no output schema, the description compensates by detailing the return format with a comprehensive example and an error case. It covers all parameters but omits guidance on alternatives and edge-case behavior, making it largely complete but not exhaustive.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The description provides plain-language explanations for all four parameters, directly mapping to schema properties. The example output illustrates the effect of extraction flags, which is critical given the 0% schema description 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 uses a specific verb 'Extract' and resource 'LinkedIn profile', clearly stating the tool's purpose. It distinguishes from sibling 'profile_search' by focusing on extracting a specific profile, not searching.

    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 a specific LinkedIn profile URL or username is available, as shown by the 'profile' parameter. However, it does not explicitly contrast with 'profile_search' or state when not to use this tool, leaving guidance only implied.

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

  • Behavior4/5

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

    With no annotations, the description carries the full burden of disclosure. It goes beyond a simple summary by describing the return type ('a list of LinkedIn profiles as dictionaries') and providing both a success example and an error example, giving agents a clear picture of expected behavior.

    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 well-organized with an intro line, Args, Returns, and examples. The success example is somewhat lengthy due to a very long title string, but it still earns its place by illustrating the exact output structure. Overall, it is efficient and clear.

    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 search tool, the description covers the core requirements: what it does, what parameter to pass, and what output to expect (both success and error formats). It lacks advanced details like pagination or empty-result behavior, but these are not critical for this low-complexity tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema only provides the parameter title 'Name' with no description. The tool description compensates fully by explaining 'name: The name to search for' in the Args section, adding the essential meaning that the schema lacks.

    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 ('Search') and resource ('LinkedIn profiles') with a scoping qualifier ('by name'). This distinguishes it from the sibling tool 'linkedin_profile', which likely targets a single profile rather than name-based search.

    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 clearly conveys when to use this tool: when you need to find LinkedIn profiles by a person's name. It provides clear context, though it does not explicitly contrast with alternatives or mention exclusions, so it stops short of 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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