Skip to main content
Glama
Minidoracat

mcp-feedback-enhanced

by Minidoracat
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Hybrid

Server Quality Checklist

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

  • Latest release: v2.6.2

  • Disambiguation5/5

    The two tools serve completely different purposes: one is for interactive feedback collection and the other for retrieving system information. There is no overlap or ambiguity between them.

    Naming Consistency3/5

    Naming conventions are mixed: 'interactive_feedback' is an adjective-noun phrase with no verb, while 'get_system_info' follows a verb_noun pattern. Both names are readable, but there is no consistent pattern across the set.

    Tool Count3/5

    With only two tools, the server feels thin for a general-purpose utility or even a feedback-focused server. The count is borderline but not extreme, as the tools themselves are fairly broad in scope.

    Completeness3/5

    The core feedback collection tool is present and well-defined, but the server offers no supplementary tools for managing, retrieving, or analyzing feedback history. The unrelated system info tool does not fill these gaps, leaving the surface slightly incomplete.

  • Average 3.8/5 across 2 of 2 tools scored.

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

    • 8 of 10 community issues answered or closed in the last 6 months
    • 14 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

  • 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?

    With no annotations, the description carries the full burden of behavioral disclosure. It does reveal the looping behavior, the need to adjust based on feedback, and the explicit exit condition. However, it never clearly states what the tool returns, whether the call blocks until user feedback, or what to do when feedback is empty or times out.

    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 front-loaded with a clear purpose line and the usage rules are organized as a numbered list, which helps parsing. However, the rules are repetitive: rules 1, 2, and 3 all restate the need to call the tool repeatedly, just with different framing. The content is useful but could be trimmed.

    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 tool has no annotations and no output schema, so the description must explain the full invocation contract. It covers the call cadence, the summary/project-directory purpose, and the exit condition. It is incomplete, though, because it does not describe the tool's return payload or define the behavior when feedback is empty or the call times out.

    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 description coverage is 100%, so summary, timeout, and project_directory are already defined in the input schema. The description adds some context by saying the project directory should be provided so the user knows what was done, but it does not add meaningful semantics for the timeout or summary parameters beyond what the schema already provides.

    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 opening line states the tool is an interactive feedback collection tool for LLM agents, and the usage rules consistently say the agent must call it to ask for feedback. This makes the verb, resource, and overall intent unambiguous and clearly distinct from the unrelated get_system_info sibling.

    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 explicitly defines when to call the tool (during any process/task/conversation, repeatedly at every step), when to call it again (after non-empty feedback), and when to stop (only on explicit end/no-more-interaction). It does not discuss alternatives, but the only sibling is an unrelated system-info tool, so the guidance is sufficient.

    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?

    No annotations are provided, so the description carries the full transparency burden. It does disclose the return format (JSON) but does not explicitly state side-effect-free behavior or other potential behavioral traits like caching or network dependence.

    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: two short lines with no filler. It front-loads the action and resource, followed by the return type.

    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?

    With zero parameters and the presence of an output schema, the description is sufficient. It states the tool's purpose and return format, making it complete for a simple getter.

    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 there is no parameter documentation needed. The baseline for 0 parameters is 4, and the description adds no unnecessary 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?

    Description clearly states the action ('獲取' / get) and the resource ('系統環境資訊' / system environment information). This distinguishes it from the sibling tool 'interactive_feedback', which is about interactive feedback.

    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?

    No guidance is provided on when to use this tool versus alternatives. There is no mention of context, prerequisites, or why one might choose this over 'interactive_feedback'.

    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/Minidoracat/mcp-feedback-enhanced'

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