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Littlecowherd

MCP Feedback Enhanced

by Littlecowherd
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.2

  • Disambiguation5/5

    The two tools serve entirely different purposes: interactive_feedback collects user feedback, while get_system_info retrieves system environment details. There is no overlap or confusion between them.

    Naming Consistency3/5

    The naming conventions are mixed: 'interactive_feedback' uses an adjective-noun structure, while 'get_system_info' uses a verb-noun structure. Although both use snake_case, the lack of a consistent verb-first pattern across the set reduces coherence.

    Tool Count3/5

    With only two tools, the server feels thin for its stated purpose of enhanced feedback collection. The count is borderline, as it sits at the low end of the acceptable range but may be insufficient for robust workflows.

    Completeness2/5

    The domain appears to be feedback collection, but the tool surface is severely limited. There is only one feedback-related tool (interactive_feedback) with no way to retrieve, update, or manage feedback history. The unrelated get_system_info tool does little to round out the domain, leaving significant gaps.

  • Average 4.2/5 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?

    The description discloses that it returns a JSON string of system information, but lacks detail on what environment data is included, whether there are any prerequisites, or potential side effects. Since no annotations exist, the description carries the full burden and only partially meets it.

    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 exceptionally concise: a single purpose line plus a return-type note. Every word adds value, and the key information is front-loaded. No redundancy or filler.

    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 simplicity (0 parameters), the presence of an output schema, and a clear statement of the return type, the description is almost complete. It only lacks a bit more detail on what 'system environment information' entails, but the output schema likely covers that.

    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, and the schema covers 100% of them (none). Per the rubric, a 0-parameter tool gets a baseline of 4, and the description needs no parameter-specific details.

    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: '獲取系統環境資訊' (get system environment information). This is a specific verb+resource combination that distinguishes it from the sibling tool interactive_feedback, which is clearly different in function.

    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 when-to-use guidance is provided, but the purpose is self-evident and the only sibling tool (interactive_feedback) is unrelated in scope. Usage context is implied rather than stated, which is acceptable for a simple read-only tool.

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

  • Behavior4/5

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

    With no annotations provided, the description carries the burden of behavioral disclosure. It explains that the tool must be invoked iteratively, that feedback is used to adjust behavior, and that the agent should provide a summary and project directory. However, it does not explicitly state that the tool blocks execution while waiting for feedback, which is implied but not fully disclosed.

    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 well-structured with numbered rules and front-loaded with a clear purpose. It is a bit longer than necessary, and rules 1 and 2 overlap in meaning ('during any process...' and 'all steps must repeatedly call this tool'), but each sentence contributes to the overall usage guidance.

    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?

    The description is sufficiently complete for a feedback tool: it explains when to call, what to include in arguments, and when to stop. However, it does not describe what happens on timeout, which is a parameter with a default of 3600 seconds but no behavioral explanation for the agent.

    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 schema already covers all three parameters with descriptions, giving a baseline of 3. The description adds value by explaining how summary and project_directory should be used in rule 5, which goes beyond the schema definitions by tying them to the agent's feedback loop.

    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: 'Interactive feedback collection tool for LLM agents.' This is a specific verb+resource pairing, and it is clearly distinct from the sibling tool get_system_info, which collects system information rather than user feedback.

    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, prescriptive guidance: call this tool during any process, repeatedly, until the user explicitly ends. It also tells the agent to summarize work and provide the project directory. This is a clear when-to-use instruction, with a defined stopping condition.

    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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