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mambalabsdev

Company Identity Resolver MCP Server

by mambalabsdev
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JavaScript
Remote

Server Quality Checklist

83%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Only one tool exists, so there is no ambiguity in tool selection.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern (resolve_company_identity), consistent with best practices.

    Tool Count5/5

    One tool is perfectly scoped for a resolver that combines all inputs (name, domain, LinkedIn) into a single operation, avoiding unnecessary complexity.

    Completeness5/5

    The tool covers the full lifecycle of identity resolution: it accepts any combination of inputs, cross-checks them, returns confidence scores, and flags conflicts. There are no missing features for its stated purpose.

  • Average 4.5/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 5 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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  ]
}

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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?

    Annotations already provide readOnlyHint, idempotentHint, etc. The description adds important behavioral details: login-free, public-data only, cross-checks inputs, flags conflicts, requires APIFY_TOKEN, and consumes credits. No contradictions with annotations.

    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 two sentences but packs significant information. While the second sentence is long, it efficiently conveys critical behavioral details without being verbose.

    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?

    No output schema, so the description must explain return values, which it does: name, domain, LinkedIn URL with confidence scores, overall score, match method. It also mentions JSON format and cost. Some minor gaps (e.g., no mention of error handling) but adequate given other richness.

    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?

    Schema coverage is 100%, so baseline is 3. The description adds value by clarifying domain is the strongest canonical key, explaining linkedin_url accepts URL or bare slug, and describing skipCache behavior. This justifies a score above baseline.

    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 resolves any combination of company name, domain, or LinkedIn URL into a canonical identity. It specifies inputs and outputs, leaving no ambiguity about its function.

    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 explains when to use the tool (to resolve company identity from partial info) and mentions cross-checking and conflict detection. However, no explicit when-not-to-use guidance is given, but this is acceptable given no sibling tools exist.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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