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Ming-321

mcp-feedback-enhanced

by Ming-321
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Hybrid

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v2.6.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one is for interactive feedback collection, the other for retrieving system information. There is no overlap or ambiguity.

    Naming Consistency2/5

    The tool names follow different conventions: 'feedback' is a simple noun, while 'get_system_info' uses a verb_noun pattern. This inconsistency could confuse an agent expecting a uniform naming scheme.

    Tool Count2/5

    With only 2 tools, the server feels under-scoped for its apparent purpose of feedback enhancement. The system info tool seems tangential, and the feedback tool alone does not justify a full server.

    Completeness2/5

    The server lacks essential tools for a feedback system, such as storing, retrieving, or analyzing past feedback. The presence of an unrelated system info tool further reduces coherence.

  • Average 3.8/5 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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  • 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, and the description does not disclose behavioral traits such as whether the tool is read-only, requires permissions, or has side effects. It only states the return type, leaving important behavioral context unspecified.

    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, consisting of two short lines. It front-loads the main purpose and avoids any filler.

    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?

    Although the output schema exists, the description does not elaborate on what 'system environment information' includes. It is adequate for a zero-parameter tool but lacks detail that would improve completeness.

    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 tool has zero parameters, so the schema coverage is effectively 100%. The baseline for zero parameters is 4, and the description does not detract from this.

    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 '獲取系統環境資訊' (Get system environment information), which is a specific verb-resource pairing. It distinguishes itself from the sibling tool 'feedback' by having a distinct purpose.

    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 explicit guidance is provided on when to use this tool versus alternatives. While the sibling tool 'feedback' implies a different use case, the description does not elaborate on context or exclusions.

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

  • Behavior3/5

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

    With no annotations, the description must disclose behavior fully. It mentions the interactive nature and timeout, but does not specify side effects (e.g., if feedback is stored), error handling, or what happens on timeout. Some behavioral transparency is provided but not complete.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is structured with headers and numbered rules, but it is verbose, repeating 'call this tool' multiple times. It could be more concise while retaining clarity.

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

    Completeness4/5

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

    Given the tool's complexity and the presence of an output schema, the description covers the feedback loop comprehensively: when and how to use, parameter roles, and termination conditions. Minor gaps like timeout behavior are acceptable.

    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 coverage is 100% with parameter descriptions in Chinese. The description adds usage context for 'message' (e.g., providing explanations) but does not significantly enhance semantics beyond the schema. Baseline score of 3 is appropriate.

    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 it is an 'Interactive feedback collection tool for LLM agents', specifying the verb (collect) and resource (feedback). It distinguishes from the sibling tool 'get_system_info' by focusing on feedback interaction.

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

    Usage Guidelines5/5

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

    The description provides explicit usage rules: when to call (during any process/task), how often (repeatedly), when to stop (explicit 'end' or 'no more interaction'), and alternatives for termination. This is a model of clear guidance.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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