Skip to main content
Glama
Dwsy

mcp-feedback-enhanced

by Dwsy
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Hybrid

Server Quality Checklist

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

  • Latest release: v2.6.4

  • Disambiguation5/5

    The two tools are entirely unrelated: interactive_feedback handles user feedback collection while get_system_info retrieves system environment details. No overlap or confusion is possible.

    Naming Consistency2/5

    The naming patterns differ significantly: 'interactive_feedback' is a compound noun, while 'get_system_info' follows a verb_noun convention. The mix of styles is inconsistent and does not suggest a unified naming scheme.

    Tool Count3/5

    With only two tools, the server feels thin and under-scoped for a 'feedback-enhanced' purpose. While not as extreme as a single tool, the count is borderline and does not convincingly cover a full feedback workflow.

    Completeness2/5

    The server name implies a focus on feedback, yet only one tool is directly related, and get_system_info is a disconnected addition. There are no tools for managing or retrieving past feedback sessions, making the surface significantly incomplete.

  • Average 4.4/5 across 2 of 2 tools scored. Lowest: 3.9/5.

    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
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

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

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    No annotations are provided, so the description must convey behavioral traits. It states the return type (JSON string), which is useful, but it does not disclose whether the operation is read-only, whether any permissions are required, or what the response contains beyond a generic 'system information'. For a simple getter, this is adequate but minimal.

    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 and well-structured: a one-line purpose followed by a clear 'Returns:' specification. There is no redundancy or unnecessary prose, and the information is front-loaded.

    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?

    For a zero-parameter system information tool, the description is complete. It clearly states the output format (JSON), which is especially helpful given that an output schema exists (though not shown). The tool's simplicity and the presence of an output schema mean the description does not need to enumerate return fields or error conditions.

    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 description need not explain parameter semantics. The input schema is empty, and per guidelines, a baseline of 4 applies. The description correctly avoids adding irrelevant parameter information.

    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 function: '獲取系統環境資訊' (Get system environment information). It uses a specific verb (get) and resource (system info), and it is clearly distinct from the sibling tool 'interactive_feedback', which is unrelated.

    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 does not mention any use cases, prerequisites, or exclusions. Since a sibling tool exists but is not referenced, the agent has no comparative context.

    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?

    With no annotations, the description fully bears the transparency burden. It discloses that reusing a conversation_id replaces prior sessions, auto-generated IDs may conflict, and user selections are returned with group numbers. It also details the quick-options feature and timeout behavior implied by the schema.

    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 lengthy but well-organized into clear sections (USAGE RULES, CONVERSATION_ID USAGE, QUICK OPTIONS FEATURE). Every paragraph adds necessary operational detail, though the repeated emphasis on calling the tool could be condensed. Minor redundancy prevents a perfect score.

    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 the presence of an output schema, the description covers all critical aspects: when to call, how to manage conversation IDs, how to present options, and what happens to selections. It is complete for the tool's complexity.

    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 covers all parameters, but the description adds substantial meaning beyond field names. It explains conversation_id handling with examples (Cursor, VS Code), how summary can embed clickable options, and the role of project_directory in user feedback. This enriches the schema's terse descriptions.

    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 opens with 'Interactive feedback collection tool for LLM agents,' clearly specifying the verb (collect) and resource (feedback). The usage rules elaborate on when to invoke it, distinguishing it from the sibling tool get_system_info which is unrelated.

    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 USAGE RULES section provides explicit when-to-use guidance: call during any process, repeatedly unless termination, and stop only on explicit user indication. It also explains how to handle feedback and summarize actions, leaving no ambiguity about alternatives.

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

GitHub Badge

Glama performs regular codebase and documentation scans to:

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

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

mcp-feedback-enhanced MCP server

Copy to your README.md:

Score Badge

mcp-feedback-enhanced MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Dwsy/mcp-feedback-enhanced'

If you have feedback or need assistance with the MCP directory API, please join our Discord server