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mambalabsdev

Job Posting Monitor MCP Server

by mambalabsdev
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusing it with others. The tool's name and description clearly define its unique purpose.

    Naming Consistency5/5

    The single tool name 'monitor_job_postings' follows a clear verb_noun pattern. There are no other tools to introduce inconsistent naming conventions.

    Tool Count3/5

    Having only one tool is minimal, but the tool is comprehensive and well-scoped for the server's stated purpose of monitoring job postings. It is not trivial, so it does not warrant a score of 1, but it sits at the borderline of being too thin.

    Completeness5/5

    The single tool covers the full workflow: discovery, filtering, enrichment, deduplication, and delta tracking. There are no obvious gaps or dead ends for the domain of job posting monitoring.

  • 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.

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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 adds substantial behavioral context beyond annotations: it requires an APIFY_TOKEN, consumes Apify credits, uses Google Jobs/SerpAPI, explains the cross-run delta cache, and clarifies that max_results and max_companies act as cost dials. This openly discloses operational side effects and aligns with the readOnlyHint.

    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 a single dense paragraph that front-loads the core purpose before diving into filtering, caching, and cost controls. Every sentence adds value, though it could be broken into clearer sentences for readability. It is appropriately sized for the tool's complexity.

    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?

    Given the tool has 13 parameters and no output schema, the description covers the essential context: output format (flat row with firmographics and LinkedIn URL), auth requirements, cost implications, default exclusions, and delta cache behavior. It does not enumerate all output fields but provides enough for an agent to understand what will be returned.

    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 input schema already covers 100% of parameters with descriptions. The tool description adds macro-level semantics, such as the relationship between max_results and max_companies as a combined cost dial and the watermark behavior of previous_run_date. This enriches but does not repeat 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?

    The description opens with a specific verb and resource: 'Find companies that are hiring for a set of role keywords across public job boards, and return one flat row per posting enriched with company firmographics and the company LinkedIn URL.' This clearly states the tool's function and output, distinguishing it from vague or tautological descriptions.

    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 the intended use case (direct outreach) by noting that staffing agencies and freelance marketplaces are dropped as noise. It also provides practical guidance on cost control via max_results and max_companies. However, it does not explicitly state when to use this tool versus alternatives, though no siblings 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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