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AyaanKhan0111

linkedin-discovery-mcp

by AyaanKhan0111
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TypeScript
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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 performs profile searches, the other reports quota status. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow the verb_noun snake_case pattern (search_linkedin_profiles, get_search_quota_status). The naming is consistent and predictable.

    Tool Count3/5

    With only 2 tools, the server is on the thin side. The narrow scope (search + quota) makes it reasonable, but it feels minimal for a discovery-focused server.

    Completeness3/5

    The core search capability is present, but there are notable gaps: no way to fetch a specific profile's details, no explicit pagination, and no advanced filters beyond search criteria. The quota tool is a helpful addition but doesn't round out the surface.

  • Average 4.5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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.

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently explains the mechanism (DuckDuckGo search, not direct scraping), the no-account-risk aspect, the shallow data depth, the return structure (match_score, matched_signals), and the self-imposed daily request budget. This goes well beyond a simple search description.

    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 moderately long but every sentence contributes value, covering purpose, limitations, and request budget. It is front-loaded with the primary function and does not waste words. The length is justified by the tool's complexity and the absence of annotations.

    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?

    Given the tool has 9 parameters, no output schema, and no annotations, the description provides a thorough context: return shape (name/headline/company/location, match_score, matched_signals), data limitations, search engine caveats, and advice on result counts. It is sufficiently complete for an agent to decide and invoke the tool appropriately.

    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 provides 100% description coverage for all 9 parameters, so the baseline is 3. The description does not add significant meaning beyond the schema, though it does provide context for max_results via the daily request budget and mentions the ranked output quality.

    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 discovers public LinkedIn profiles via DuckDuckGo's public search, using a specific verb and resource. It also distinguishes itself from sibling tools by explicitly noting it does not log into or scrape LinkedIn directly.

    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 context on when to use the tool (discovering public LinkedIn profiles) and when not to rely on it (needs verified enriched profiles or full work history). It also advises broadening criteria if few results are returned. However, it does not explicitly name an alternative tool, only suggests manual verification.

    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 provided, the description carries the full burden. It discloses that the budget is self-imposed and resets daily ('today's'), and implies a non-destructive read. It doesn't detail output format or potential side effects, but for a status check, the key behaviors are covered.

    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 sentences, front-loaded with the primary action and outcome, followed by a practical usage tip. No wasted words or redundancy.

    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?

    For a zero-parameter, no-output-schema tool, the description fully conveys what it reports (usage and remaining) and when to use it (before large batches). The complexity is low, and the description leaves no major gaps.

    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 tool has zero parameters, so the schema already exhaustively covers all inputs. The description adds no parameter meaning, but with no parameters present, this is appropriate. Baseline of 4 is warranted.

    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 the specific verb 'Reports' and clearly identifies the resource: today's self-imposed DuckDuckGo request budget. It distinguishes itself from the sibling tool search_linkedin_profiles by indicating this is a monitoring/status tool rather than a search tool.

    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 says to check before large search batches, providing clear context on when to use. It doesn't mention when not to use or alternative tools, but the context is sufficient for a simple status tool with no direct alternatives.

    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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