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HyperHeroX

user-web-feedback

by HyperHeroX
OverviewSchemaRelated ServersScoreDiscussions
TypeScript

Server Quality Checklist

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

  • Latest release: v2.8.23

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools. The single tool's purpose is clearly stated.

    Naming Consistency5/5

    The only tool name 'collect_feedback' follows a consistent verb_noun pattern, and there are no other tools to create inconsistency.

    Tool Count4/5

    A single tool for a focused purpose like collecting user feedback is acceptable, though minimal. It falls within the reasonable low end of the scale.

    Completeness2/5

    The server only provides a way to collect feedback but lacks any tools for viewing, managing, or deleting collected feedback, leaving significant gaps for typical workflows.

  • Average 4.1/5 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 is passing

  • 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

  • Behavior4/5

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

    With no annotations, the description carries full burden. It clearly warns that work_summary is the only content shown to users and provides detailed formatting requirements. It mentions opening a web interface, implying user interaction. However, it does not disclose side effects, such as whether feedback is stored or if the tool is blocking.

    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 front-loaded with purpose and a critical warning, then provides extensive formatting guidance. While long, every sentence adds value. Slightly verbose but justified by the need to ensure proper usage.

    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 no output schema and 3 parameters, the description thoroughly covers the sole required parameter. However, it omits what happens after feedback is submitted (e.g., no return value described) and does not mention if the web interface provides a response. Adequate but could clarify the full workflow.

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

    Parameters5/5

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

    Schema description coverage is 100%, and the description adds enormous context: work_summary is the only content shown, must be a complete Markdown report, and includes required sections and minimum length. This goes far beyond the schema definition.

    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: 'Collect feedback from users about AI work' and 'opens a web interface for users to provide feedback.' The verb 'Collect' and resource 'feedback' are specific, and the description distinguishes the unique behavior of the work_summary field.

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

    Usage Guidelines3/5

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

    No sibling tools are provided, so differentiation is not required. However, the description gives no explicit guidance on when this tool should be used versus alternatives. It implies use for collecting user feedback, but lacks 'when to use' or 'when not to use' 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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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