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youjunxiaji

gl-mcp-feedback

by youjunxiaji
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v2.2.3

  • Disambiguation5/5

    Each tool serves a clearly distinct purpose: interactive_feedback collects user feedback via a web UI, and get_system_info retrieves system environment details. There is no overlap or ambiguity between them.

    Naming Consistency2/5

    The tool names do not follow a consistent pattern. 'interactive_feedback' is an adjective-noun phrase while 'get_system_info' follows a verb_noun convention, mixing styles and making the set feel inconsistent.

    Tool Count3/5

    With only 2 tools, the server is on the thin side. The system info tool seems unrelated to the core feedback domain, so the count feels slightly inflated for the apparent purpose.

    Completeness4/5

    The interactive_feedback tool provides a complete feedback loop, including handling skips, termination, and rich summaries, so there are no obvious gaps in the primary purpose. The system info tool is an extra that doesn't address any feedback-related gap.

  • Average 4.4/5 across 2 of 2 tools scored. Lowest: 3.8/5.

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

    • No community issues in the last 6 months
    • 2 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

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  • This repository includes a README.md file.

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  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
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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

  • Behavior3/5

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

    With no annotations, the description carries full burden. It discloses the return type (string in JSON format), and the verb 'get' implies a read-only operation. However, it does not explicitly state that it is non-destructive or describe any potential side effects, which would be helpful for a tool with no annotations.

    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 core purpose and immediately provides return format information, with no wasted words.

    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?

    For a simple, parameterless tool with an output schema (indicated by the Returns section), the description is sufficiently complete. It tells the agent what the tool returns and in what format, which is all that is needed for this straightforward operation.

    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 baseline is 4. The description does not need to explain parameters, and indeed mentions none, which is appropriate for a parameterless tool.

    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 '获取系统环境信息' (Get system environment information), which is a specific verb and resource. It unambiguously distinguishes from the sibling tool 'interactive_feedback', which is about user feedback rather than system data.

    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 versus alternatives. It only states what the tool does without mentioning any exclusions, prerequisites, or comparisons to sibling tools.

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

  • Behavior5/5

    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 transparently discloses runtime validation (empty/placeholder rejection), the meaning of an empty result (user skipped → re-call), timeout behavior, and explicit termination terms. This goes well beyond basic behavior.

    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 well-structured with clear sections (Strict prohibitions, summary rules, title rules, Args, Returns). It is front-loaded with the purpose and every section contains actionable rules without fluff, making the length 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 no annotations and no output schema, the description fully compensates by covering purpose, usage, parameter semantics, return behavior (including image uploads), and error handling. It leaves little ambiguity for an agent selecting and invoking the tool.

    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?

    Schema description coverage is 100%, so baseline is 3. The description adds value beyond the schema by providing writing rules (e.g., Markdown requirements, title ≤30 chars with examples), reinforcing that summary is effectively required despite its schema default, and setting a minimum timeout (≥600).

    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 'Open a Web UI to collect interactive feedback from the user,' which is a specific verb plus resource. It also positions itself as the PRIMARY communication channel, distinguishing it from the sibling get_system_info and explicitly contrasting it with Cursor's AskQuestion.

    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?

    Provides explicit when-to-use and when-not-to-use guidance: 'DO NOT invoke Cursor's built-in AskQuestion' and 'DO NOT end the turn with a plain-text reply.' It also details the re-call policy on user skip and notes that full scenarios live in external rules, but the operational guidelines here are comprehensive.

    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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