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mambalabsdev

mcp-meta-ad-library-monitor

Server Quality Checklist

83%
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  • Latest release: v1.0.1

  • Disambiguation5/5

    With only one tool, there is no possibility of misselection — an agent will always choose find_meta_ads. Its purpose is clearly described and its output format is explicit, so there is zero ambiguity in tool choice.

    Naming Consistency4/5

    The single tool name follows the standard verb_noun snake_case convention (find_meta_ads), which is clean and conventional. However, one tool cannot demonstrate consistency 'throughout' a set, so the pattern is plausible but unproven rather than fully established.

    Tool Count3/5

    One tool feels thin for a server presenting itself as an ad-library 'monitor.' That said, find_meta_ads is substantial and covers a full search-and-retrieve workflow, so it is not the trivial single-tool extreme case.

    Completeness3/5

    The tool thoroughly covers the core search workflow — creative details, delivery dates, surfaces, snapshot URL, and coverage caveats — and is honest about API limitations. However, the 'monitor' framing implies ongoing tracking or follow-up capabilities that are absent: no fetch-by-ID, no time-based comparison, and no way to manage watched advertisers, which an agent cannot work around.

  • 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
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
  • 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.

  • This server has been verified by its author.

  • Add related servers to improve discoverability.

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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 annotations already mark this read-only, idempotent, and non-destructive, and the description adds substantial behavioral context beyond that: it requires the caller's own Meta token, consumes Apify credits, explains that spend/impressions are bands only for political ads, and clarifies that a null spend is not zero. These are exactly the non-obvious behaviors an agent needs to interpret results correctly.

    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 dense but every sentence earns its place: purpose, output format, coverage caveat, auth requirement, spend interpretation, and cost implications. It front-loads the core function and returns details before constraints, making it easy for an agent to quickly classify the tool.

    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?

    There is no output schema, so the description correctly explains the return shape and key interpretation rules. It also covers external prerequisites, cost, coverage limits, and the distinction between political and non-political data. For a tool with nine parameters and external dependencies, this is complete enough for an agent to select and invoke it correctly.

    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 the baseline is 3. The main description does not need to restate parameter meanings; it does add global context about the EU coverage default and the identity gate, but the individual parameter semantics are already fully documented in the schema. Nothing is missing, but the description itself does not carry the parameter burden.

    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: 'Search the Meta Ad Library for a company's ads through Facebook's sanctioned Graph API.' It also defines the output shape precisely: 'ONE FLAT ROW PER AD with creative text, headline, delivery dates...' This goes well beyond a vague restatement and makes the tool's function unmistakable even without sibling tools.

    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?

    There are no sibling tools to contrast against, but the description gives strong contextual usage guidance: it warns that coverage is not universal, that a commercial advertiser outside the EU may legitimately be absent, and that political/issue data is a separate archive. It stops short of an explicit 'use this when... / don't use this when...' checklist, so it earns a 4 rather than a 5.

    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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