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ACSGenUI

AEM Block Collection MCP Server

by ACSGenUI
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Server Quality Checklist

58%
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 ambiguity or overlap between tools. The single tool 'list_blocks' has a clear and distinct purpose that cannot be confused with any other tool in this set.

    Naming Consistency5/5

    The single tool name 'list_blocks' follows a clear verb_noun pattern. Since there is only one tool, consistency is inherently perfect with no deviations or mixed conventions to evaluate.

    Tool Count2/5

    A single tool is too few for a server with 'Collection' in its name, suggesting it should manage AEM blocks. This minimal surface lacks essential operations like create, update, delete, or get specific blocks, severely limiting functionality for the apparent domain.

    Completeness1/5

    The tool set is severely incomplete for managing AEM blocks. It only provides listing functionality, missing all CRUD operations (create, read, update, delete) and other lifecycle actions. This will cause significant agent failures when trying to perform basic block management tasks.

  • Average 3.2/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 0 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

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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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the source ('blocks.json') and output includes metadata, but fails to describe key behaviors like whether this is a read-only operation, potential performance impacts, error handling, or format of the returned data. This leaves significant gaps for an agent to understand how the tool behaves.

    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 a single, efficient sentence that front-loads the key information ('List all available AEM blocks') and adds necessary detail ('with metadata from blocks.json') without any wasted words. It's appropriately sized for a simple tool.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. It states the purpose and source, but lacks details on behavioral traits and output format, which are important for an agent to use it correctly. It meets the basic requirement but has clear gaps in completeness.

    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 tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and a baseline of 4 is appropriate as it doesn't mislead or omit parameter information.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('List') and resource ('AEM blocks'), and specifies the source ('blocks.json') and what's included ('with metadata'). It's specific about what the tool does, though without sibling tools to differentiate from, it can't achieve a perfect 5.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool, such as prerequisites, context, or alternatives. It simply states what the tool does without indicating any usage scenarios or constraints.

    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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