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mambalabsdev

Funding & Press Signal Scanner 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: v1.0.1

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion between tools.

    Naming Consistency5/5

    A single tool name follows a clear and consistent pattern, with no other tools to cause inconsistency.

    Tool Count5/5

    The single tool adequately covers the server's stated purpose of scanning for funding and press signals, making the count appropriate.

    Completeness5/5

    The tool covers all major signal types (funding, executive moves, launches, acquisitions) and returns deduplicated, dated events, satisfying the domain requirements.

  • 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

  • Behavior4/5

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

    The description adds behavioral traits beyond annotations: it states 'Read-only' (matching readOnlyHint), 'requires APIFY_TOKEN', 'consumes Apify credits', and that results are 'deduplicated, dated events'. These go beyond the annotation hints, providing useful operational context.

    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 sentences with no wasted words. It front-loads the purpose and then adds key details (output format, read-only, requirements). Every sentence adds value.

    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 low complexity, complete schema/annotation coverage, and no output schema, the description fully covers input, output, and constraints. It explains what data is returned, how it's formatted, and what infrastructure is needed.

    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%, and the description adds extra meaning: for 'domain' it specifies 'without https or www' and gives an example; for 'company_name' it explains it is optional and used when domain does not match brand, with an example. This enhances understanding beyond the schema definitions.

    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 scans Google News and PR wires for specific signals (funding rounds, executive moves, etc.) and returns deduplicated, dated events in a flat JSON format. The verb 'scan' and the resource 'news/PR wires' are specific, and there are no sibling tools to differentiate.

    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 implies the tool is used when needing to find funding/press signals for a company domain. It also mentions prerequisites (APIFY_TOKEN, credit consumption) but does not explicitly state when not to use it or provide alternatives. The context is clear enough for straightforward selection.

    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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