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zhiyingzzhou

AI Notify MCP

by zhiyingzzhou
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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation1/5

    The two tools are essentially identical in purpose. Both 'auto_notify_completion' and 'show_completion_notification' describe showing a notification when AI completes a response, with only minor wording differences. An agent would have no meaningful basis to choose between them, leading to confusion and misselection.

    Naming Consistency3/5

    The naming shows mixed conventions. 'auto_notify_completion' uses a verb-object pattern with underscores, while 'show_completion_notification' uses a verb-noun pattern with underscores. Both are readable, but the inconsistency in structure (auto vs. show, notify vs. notification) reduces predictability.

    Tool Count2/5

    With only 2 tools, the server feels thin for its purpose of AI notifications. Given the tools are redundant, this exacerbates the issue—it's essentially one tool split into two. A more appropriate count would be 1-2 distinct tools, but here the duplication makes it seem artificially inflated.

    Completeness2/5

    The server's domain appears to be AI response notifications, but the tool set is severely incomplete. There are no tools for configuring notifications (e.g., setting preferences, disabling), handling different types of notifications, or managing notification history. The redundancy in the two tools does not add meaningful coverage, leaving significant gaps for agent workflows.

  • Average 3.2/5 across 2 of 2 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?

    No annotations are provided, so the description carries the full burden. It mentions the tool 'shows' a notification, implying a display action, but lacks details on behavioral traits such as what triggers the notification, if it's user-visible, whether it requires specific permissions, or if it has side effects like logging. This leaves significant gaps in understanding how the tool behaves beyond its basic function.

    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 brief and to the point, consisting of one clear sentence that states the purpose and usage. It is appropriately sized without unnecessary words, though it could be slightly more structured by separating purpose from guidelines for better readability.

    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 has no annotations, no output schema, and a simple input schema, the description provides basic purpose and usage but lacks completeness. It doesn't cover what the notification looks like, how it's triggered, or any error conditions, which are important for a tool that interacts with user interfaces. The context is minimal but adequate for a simple tool.

    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?

    The input schema has 100% description coverage, with the parameter 'responseLength' documented as optional with a default. The description adds no additional meaning about parameters beyond what the schema provides, such as explaining why response length matters or how it affects the notification. Thus, it meets the baseline for high schema coverage but doesn't enhance parameter understanding.

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

    Purpose3/5

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

    The description states the action ('Automatically show completion notification') and provides a usage instruction ('call this after providing any response'), which clarifies the purpose. However, it doesn't specifically differentiate from the sibling tool 'show_completion_notification', leaving ambiguity about how they differ (e.g., 'automatically' vs. manual).

    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 explicitly states when to use the tool ('after providing any response'), which provides clear context for its application. However, it doesn't mention when not to use it or how it differs from the sibling tool 'show_completion_notification', missing explicit alternatives or exclusions.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the action ('Show a system notification') but lacks details on platform-specific behavior, error handling, user permissions required, or whether it's synchronous/asynchronous. For a tool with no annotations, this leaves significant gaps in understanding its operational traits.

    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, clear sentence that efficiently conveys the core functionality without unnecessary words. It is front-loaded with the main action and condition, making it easy to parse and understand quickly.

    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 low complexity (3 parameters, no output schema, no annotations), the description is minimally adequate but incomplete. It covers the basic purpose but lacks usage guidelines, behavioral details, and output information, which are needed for a tool that interacts with system notifications. The schema compensates for parameter semantics, but overall completeness is limited.

    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?

    The input schema has 100% description coverage, documenting all three parameters (title, message, sound) with defaults. The description does not add any meaning beyond the schema, such as explaining parameter interactions or usage examples. With high schema coverage, the baseline score of 3 is appropriate as the schema handles the heavy lifting.

    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's purpose with a specific verb ('Show') and resource ('a system notification'), and it specifies the trigger condition ('when AI completes a response'). This distinguishes it from the sibling tool 'auto_notify_completion', which might imply automation rather than manual invocation.

    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 versus the sibling 'auto_notify_completion', nor does it mention any prerequisites, exclusions, or alternative scenarios. It merely states what the tool does without contextual usage advice.

    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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