mcp-win-toast
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools serve clearly distinct purposes: show_dialog displays a modal dialog box with an OK button, while show_toast shows a non-modal toast notification. An agent can easily differentiate them based on the behavior.
Naming Consistency5/5Both tool names follow the consistent verb_noun pattern (show_dialog, show_toast), making the naming predictable and easy to understand.
Tool Count4/5With only 2 tools, the server is minimal, but for its specific domain of Windows notification types, the count is reasonable and not excessive. It could benefit from additional dialog variations, but the current scope is justifiable.
Completeness4/5The server covers the two most common Windows notification types (dialog and toast). Minor gaps exist, such as the lack of dialogs with custom buttons or toast with actions, but the core functionality is present.
Average 3.1/5 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
This repository is licensed under ISC 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 should disclose behavioral traits. It only states that a dialog is displayed with an OK button, but omits details like whether the dialog is modal, whether it blocks execution, or what happens after the button is clicked.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence in Japanese that efficiently conveys the tool's purpose. It is appropriately concise, though a bit more structure could improve readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 required parameters, no output schema), the description is minimally complete. It tells what the tool does, but does not explain return values or interaction behavior. For a dialog tool, this is acceptable but not exemplary.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of parameters with descriptions for both 'title' and 'message'. The description adds no additional meaning beyond what the schema already provides, so the baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool displays a Windows standard dialog box with an OK button. It specifies the verb (display) and resource (dialog box). However, it does not explicitly differentiate from the sibling tool 'show_toast', though the context implies a difference.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 alternative 'show_toast'. There is no mention of suitable scenarios, prerequisites, or when not to use it.
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, and the description does not disclose any behavioral traits beyond the basic action. It lacks information on potential side effects, permissions, or whether multiple toasts stack.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that immediately communicates the tool's purpose. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with two required string parameters and no output schema, the description is minimally sufficient but lacks guidance on edge cases, behavior expectations, or integration context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with Japanese descriptions for both 'title' and 'message'. The description does not add additional meaning beyond what the schema already provides, so a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Displays a Windows toast notification' is a specific verb+resource combination that clearly states what the tool does. It distinguishes from sibling 'show_dialog' which likely shows a dialog, not a toast.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 'show_dialog' or other alternatives. There is no mention of context, prerequisites, or when not to use it.
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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