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Cactusinhand

MCP Notify Server

by Cactusinhand
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Python
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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 'send_notification' has a clear, distinct purpose that cannot be confused with any other tool in the set.

    Naming Consistency5/5

    The tool name 'send_notification' follows a clear verb_noun pattern (send + notification). 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 named 'MCP Notify Server', which suggests a broader notification domain. This minimal set feels thin and likely incomplete for typical notification workflows, such as managing notifications or checking statuses.

    Completeness2/5

    The tool surface is severely incomplete for a notification server. While 'send_notification' covers sending, there are obvious gaps like listing notifications, updating them, deleting them, or configuring notification settings, which agents would need for full functionality.

  • Average 2.9/5 across 1 of 1 tools scored.

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

    • 0 of 2 community issues answered or closed 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

  • 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

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden. It states the tool sends a notification but doesn't disclose behavioral traits like whether it's synchronous/asynchronous, what happens on failure, if it requires specific permissions, or how notifications are delivered. 'Optional sound' hints at a feature but lacks details on default behavior or sound types.

    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 extremely concise with a single sentence that directly states the tool's function. It's front-loaded and wastes no words, making it easy to parse quickly. Every part of the sentence contributes to understanding the tool's purpose.

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

    Completeness2/5

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

    Given no annotations, no output schema, and 0% schema description coverage for 4 parameters, the description is incomplete. It covers the basic action but lacks details on behavior, parameters, return values, or error handling. For a notification-sending tool with multiple parameters, more context is needed to guide effective use.

    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 0%, but the description doesn't add meaning beyond what the schema provides. It mentions 'optional sound' which corresponds to the 'play_sound' parameter, but doesn't explain other parameters like 'title', 'message', or 'timeout'. With 4 parameters and no schema descriptions, the description fails to compensate adequately, resulting in a baseline score.

    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 ('send') and resource ('system notification') with an additional feature ('optional sound'). It's specific about what the tool does, though without sibling tools, differentiation isn't applicable. The purpose is unambiguous but could be more detailed about the notification type or system context.

    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 appropriate contexts, prerequisites, or alternatives. It mentions 'optional sound' but doesn't explain when sound should be enabled or disabled. With no sibling tools, this is less critical, but still lacks usage context.

    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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