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mambalabsdev

Company Social Presence Mapper MCP Server

by mambalabsdev
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JavaScript
Remote

Server Quality Checklist

83%
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  • Latest release: v1.0.1

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools. The tool's purpose is clearly defined, so an agent can unambiguously select it for the task of mapping social presence.

    Naming Consistency5/5

    The single tool name, map_company_social_presence, uses clear and descriptive snake_case. There are no other tools to compare, so no inconsistency exists.

    Tool Count3/5

    One tool for the domain of social presence mapping is borderline. While the tool is comprehensive, a single tool covering all platforms may be seen as too coarse. The count is acceptable but not ideal.

    Completeness3/5

    The tool covers the core functionality of discovering profiles and fetching follower counts, but with noted limitations (e.g., X follower count missing, Instagram and Facebook counts best-effort). For the stated purpose, it is mostly complete but has gaps.

  • Average 4.7/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 8 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.

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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?

    The description expands significantly on annotations by detailing discovery methods (homepage links, web search, pattern guessing, validation), follower count extraction limitations (X URL-only, Instagram/Facebook best-effort), and caching behavior (7-day cache, skipCache parameter). This fully discloses behavior beyond readOnlyHint and idempotentHint.

    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 concise paragraphs, front-loaded with the primary action and output. Every sentence provides value—no filler. It efficiently covers purpose, platforms, discovery, limitations, and requirements.

    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 no output schema, the description fully explains the return format ('flat Clay-ready JSON with URLs and follower counts') and all key behaviors. It addresses all five parameters, cost, caching, and fallback logic, making it complete for an AI agent.

    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?

    With 100% schema coverage, baseline is 3. The description adds operational context: company_name improves disambiguation, includeFollowerCounts affects cost, skipCache overrides cache. This enriches parameter semantics beyond the schema descriptions.

    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 maps a company's social media presence across LinkedIn, X, Instagram, Facebook, and YouTube, returning profile URLs and follower counts. It specifies the exact platforms and outputs, 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?

    The description notes that the tool is read-only, requires an APIFY_TOKEN, and consumes credits. While no sibling tools are listed, it implicitly advises use for social presence mapping without alternatives. The caveats about X, Instagram, and Facebook counts provide usage context.

    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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  • Evaluate tool definition quality.

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