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jens-duttke

mcp-popup-ui

by jens-duttke
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.1.0

  • Disambiguation5/5

    The two tools are clearly differentiated by singular vs. plural selection. Ask_user restricts to exactly one option, while ask_user_multiple allows multiple selections, with descriptions emphasizing this distinction.

    Naming Consistency5/5

    Both tools follow a consistent snake_case verb_noun pattern: ask_user and ask_user_multiple. The naming clearly communicates the shared purpose and the single/multiple distinction.

    Tool Count3/5

    With only 2 tools, the server feels thin, especially given the broader 'popup-ui' name. The two selection modes cover a narrow niche, and while they are well-defined, the overall surface is minimal.

    Completeness3/5

    The tools handle single and multiple choice popups, but other common popup interactions such as free-form text input, simple confirmations, or informational dialogs are missing. Agents must rely on allow_other for custom input, which is not ideal, leaving notable gaps.

  • Average 4.2/5 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

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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

  • Behavior4/5

    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 discloses the popup interaction ('opens a popup in the user's browser and waits for their selections'), the 'Other' option behavior, and the return value ('Returns an array of selected options, or indicates if the user skipped'). This is adequate, though it could explain the skip signal in more detail.

    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 well-structured with clear sections (WHEN TO USE, EXAMPLES, CRITICAL, PARAMETERS) and front-loaded with the core purpose. The warning about parameter separation with wrong/correct examples is valuable but slightly verbose. Overall, each section earns its place.

    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?

    The description covers behavior, usage contexts, and return values, but it fails to align with the actual schema by listing parameters that don't exist (min_selections, max_selections). This mismatch creates a completeness gap. Core interaction is explained, but the inaccuracies prevent a higher score.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 100%, so baseline is 3. However, the description introduces parameters 'min_selections' and 'max_selections' that do not exist in the input schema, potentially misleading the agent. The description largely restates schema fields (label, description, recommended) and the 'CRITICAL' warning, while adding no genuinely useful parameter semantics beyond schema. The presence of non-existent parameters lowers the score.

    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 verb and resource: 'Ask the user to choose ONE OR MORE options from a list.' It explicitly distinguishes from sibling tool 'ask_user' by emphasizing multiple selections ('whenever the user can select multiple items'). Examples reinforce the purpose without ambiguity.

    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?

    Provides a dedicated 'WHEN TO USE THIS TOOL' section with specific scenarios and examples ('Which features do you want?', 'Select the files to include'). It clearly implies single-selection cases are not for this tool, but it does not explicitly name the sibling tool or state exclusions, so it stops short of a 5.

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

  • Behavior4/5

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

    With no annotations provided, the description carries the full transparency burden. It discloses that the tool 'opens a popup in the user's browser and waits for their selection,' describes the behavior for allow_other, and states the return value (selected option or skip indication). It omits potential timeout/cancellation behavior, but the disclosed core interaction is clearly explained.

    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 longer than average but well-structured with headings, examples, and a prominent warning. It front-loads the core purpose and usage. Some repetition exists (e.g., multiple examples of single-choice questions), but each section serves a distinct role, so the length is justified.

    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?

    Given the tool's interactive nature and five parameters, the description is remarkably complete. It covers what the tool does, when to use it, how to pass parameters correctly, what the popup does, and what it returns. The sibling distinction is implicit via 'exactly ONE,' and the output behavior is addressed, making it adequate even with the output schema present.

    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 input schema already covers all parameters at 100% coverage, so the baseline is 3. The description adds genuine value with its CRITICAL warning that parameters must be passed separately, including explicit wrong and correct examples. This clarifies a common misuse that the schema alone does not fully convey, elevating it to 4.

    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: 'Ask the user to choose exactly ONE option from a list.' It explicitly contrasts with text-based listing and the sibling tool by emphasizing 'exactly ONE,' making the tool's role and scope unambiguous.

    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 provides extensive when-to-use guidance, including concrete triggers and examples like 'Should I use Option A, B, or C?' It does not explicitly mention the sibling tool ask_user_multiple by name or state 'do not use when multiple selections are needed,' but the emphatic 'exactly ONE' and mutual-exclusivity wording imply the exclusion.

    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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