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ISimon3

Interactive Feedback MCP

by ISimon3
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Python
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Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as requesting interactive feedback with support for text and images, and no other tools exist to cause confusion.

    Naming Consistency5/5

    The single tool name 'interactive_feedback' follows a clear and consistent snake_case pattern. Since there is only one tool, there is no inconsistency to evaluate, and the naming is straightforward and descriptive.

    Tool Count2/5

    A single tool is too few for a server named 'Interactive Feedback MCP', which suggests a broader scope for interactive feedback mechanisms. This minimal set may limit functionality and force agents to rely heavily on this one tool for all feedback-related tasks, which is insufficient for typical MCP server purposes.

    Completeness2/5

    The tool surface is severely incomplete for an interactive feedback domain. While 'interactive_feedback' handles requesting feedback, there are obvious gaps such as tools for managing feedback (e.g., list, update, delete), analyzing feedback, or supporting other feedback types beyond text and images. This will likely cause agent failures in comprehensive feedback workflows.

  • Average 2.9/5 across 1 of 1 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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool '支持文本和图片' (supports text and images), which adds some context about input types. However, it does not describe critical behavioral traits such as whether this is a blocking operation, how feedback is collected or returned, error handling, or any permissions required. For a tool with no annotations, this leaves significant gaps.

    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 front-loaded: '向用户请求交互式反馈，支持文本和图片' (Request interactive feedback from users, supporting text and images). It is a single sentence with no wasted words, clearly stating the core functionality. Every part of the description earns its place by specifying the action and supported formats.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the complexity of a feedback collection tool with no annotations and no output schema, the description is incomplete. It lacks information about how the feedback is returned (e.g., format, structure), any side effects, or error conditions. While it mentions support for text and images, it does not cover the full behavioral context needed for an agent to use the tool effectively.

    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?

    The schema description coverage is 100%, meaning both parameters ('message' and 'predefined_options') are fully documented in the schema. The description does not add any additional meaning or clarification beyond what the schema provides (e.g., it does not explain how 'predefined_options' should be formatted or used). With high schema coverage, the baseline score of 3 is appropriate as the description does not compensate but also does not detract.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose: '向用户请求交互式反馈，支持文本和图片' (Request interactive feedback from users, supporting text and images). It specifies the verb ('请求' - request) and resource ('交互式反馈' - interactive feedback) with additional detail about supported input types. However, since there are no sibling tools, it cannot demonstrate differentiation from alternatives.

    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 prerequisites, exclusions, or contextual factors that would help an agent decide when this tool is appropriate. The absence of sibling tools means no explicit alternatives are named, but general usage context is still missing.

    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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