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YangHang0210

mcp-feedback-ultra

by YangHang0210
OverviewSchemaRelated ServersScoreDiscussions
JavaScript

Server Quality Checklist

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

  • Latest release: v2.8.0

  • Disambiguation5/5

    The two tools serve completely different purposes—feedback collection and system information—so there is no risk of misselection. An agent would never confuse them.

    Naming Consistency3/5

    Both tools use snake_case, but the patterns differ: 'get_system_info' follows a verb_noun style, while 'interactive_feedback' is adjective_noun. This inconsistency makes the naming slightly less predictable.

    Tool Count3/5

    With only two tools, the set is minimal and borderline appropriate. The inclusion of an unrelated system utility alongside the core feedback tool dilutes the server's focus.

    Completeness4/5

    The interactive_feedback tool appears to cover the core feedback loop with its built-in usage rules, making the feedback process self-contained. However, there are no supporting tools for managing or analyzing feedback, and the system info tool doesn't contribute to the domain.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • No commit activity data available
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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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, so the description carries the burden of behavioral disclosure. It only mentions the return format (JSON string) but does not disclose whether the operation is read-only, if it has side effects, or requires any permissions. For a tool that retrieves system info, the lack of explicit safety traits is a significant omission.

    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, using only two brief clauses to state the purpose and return type. It is front-loaded with the core function and provides minimal but necessary detail. No unnecessary words are included, and it adheres to the principle that every sentence should earn its place.

    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 (zero parameters, no side effects likely), the description is adequate. It states what the tool does and the return format. However, it could be more complete by explicitly indicating that it is a safe, read-only operation, which would reassure an agent. Since this is not stated, it falls slightly short of a perfect score.

    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 input schema coverage is 100% (empty). The description adds no parameter-specific information, but none is needed. The baseline for zero parameters is 4, and this description meets it without requiring further elaboration.

    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: '獲取系統环境資訊' (get system environment information). The verb '獲取' (get) and resource '系統資訊' (system info) are specific, and the tool is easily distinguished from the sibling '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?

    There is no guidance on when to use this tool or when not to. The description only provides a basic statement of what it does, without any context, prerequisites, or alternatives. This is a clear gap in usage direction.

    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 provided, the description carries full responsibility for behavioral disclosure. It reveals mandatory call patterns, stopping conditions, reaction to non-empty feedback, and [NEW TASK] handling—going far beyond a simple tool summary.

    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 longer than typical but well-organized with sections and numbered rules. Some repetition exists (e.g., 'must call this tool' repeated across rules), but the structure makes the behavioral mandates clear and easy to follow.

    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?

    The tool has no output schema, but the description explicitly states the return type (list of TextContent and MCPImage). It fully covers usage rules, argument purposes, default timeout, and edge cases like [NEW TASK], making it self-contained for an agent.

    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 the baseline is 3. The description adds some context—e.g., project_directory is for user context and summary is for user review—but largely restates information already present in the input schema defaults and 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 clearly identifies the tool as an 'Interactive feedback collection tool for LLM agents,' specifying its resource (user feedback) and purpose. It is sharply distinguished 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?

    Contains explicit USAGE RULES with numbered steps detailing when to call the tool (during any process, after feedback, until termination), when to stop ('end' or 'no more interaction needed'), and how to handle [NEW TASK] prefixes. This is exemplary 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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