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mambalabsdev

mcp-gtm-hiring-signal-scraper

by mambalabsdev
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JavaScript
Remote

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.6

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools. The tool's purpose is unique and clearly defined.

    Naming Consistency5/5

    The single tool name 'scan_gtm_hiring_signals' follows a clear verb_noun pattern and is descriptive, making it consistent within the server.

    Tool Count3/5

    The server has only one tool, which is on the low end. While the tool is non-trivial and serves a specific function, the scope of the server might benefit from additional tools (e.g., for configuration or filtering) to be more comprehensive.

    Completeness3/5

    The single tool covers the core task of scanning for GTM hiring signals, but lacks auxiliary features like specifying target companies or managing multiple scans, which could be considered notable gaps.

  • Average 4.3/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 29 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.

  • This repository includes a glama.json configuration file.

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  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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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?

    Beyond annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), the description adds valuable behavioral details: requires APIFY_TOKEN, consumes Apify credits, and supports specific ATS platforms. No contradictions with annotations. The description enriches transparency by specifying auth and cost implications.

    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 concise (4 sentences), front-loaded with the main action, and includes only essential information (purpose, output, platforms, requirements). Every sentence adds value with no fluff.

    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 absence of an output schema, the description compensates by specifying the output format (Clay-ready flat JSON). It also covers supported platforms, auth requirements, and credit consumption. For a scanning tool with 3 parameters and clear annotations, the description provides all necessary context for correct invocation.

    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?

    Schema description coverage is 100%, so baseline is 3. The description does not add significant new meaning beyond the parameter descriptions, though it reinforces the ATS platform support. No additional parameter semantics or examples are provided that aren't already in the schema.

    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?

    Description clearly states the tool scans company career pages for GTM hiring activity, specifying the types of jobs (sales, marketing, revenue operations) and supported ATS platforms (Greenhouse, Lever, Ashby). Output format is explicitly described as Clay-ready flat JSON. With no sibling tools, differentiation is unnecessary, and the purpose is fully conveyed.

    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?

    Description provides explicit context on when to use (detecting GTM hiring signals) and operational requirements (requires APIFY_TOKEN, consumes credits). While no alternative tools are listed (siblings are absent), the description clearly implies the use case and prerequisites, making the guidance adequate.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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