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madasor

Interactive Feedback MCP

by madasor
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Python
Local

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 for ambiguity or overlap between tools. The tool's purpose is clearly distinct by default.

    Naming Consistency5/5

    A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The name 'interactive_feedback' follows a clear verb_noun pattern.

    Tool Count2/5

    One tool is too few for a server named 'Interactive Feedback MCP', which suggests a broader scope for interactive feedback operations. This minimal set feels thin and incomplete for the implied domain.

    Completeness2/5

    The tool surface is severely incomplete for an interactive feedback domain. It only provides a request function, with no obvious operations for managing, updating, or retrieving feedback, creating significant gaps for agent workflows.

  • Average 2.9/5 across 1 of 1 tools scored.

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

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'interactive feedback' but doesn't explain what that entails—whether it initiates a chat, sends a notification, requires user input, or has side effects like creating records. This leaves critical behavioral traits unspecified.

    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 a single, direct sentence that efficiently conveys the core function without unnecessary words. It is front-loaded and appropriately sized for the tool's complexity, with no wasted verbiage.

    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 lack of annotations and output schema, the description is incomplete. It fails to explain what 'interactive feedback' means in practice, what the tool returns, or any behavioral nuances, leaving significant gaps for an AI agent to understand how to use it 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%, with clear descriptions for both parameters in the input schema. The description adds no additional meaning beyond the schema, such as format examples or constraints, but the schema adequately covers the basics, justifying the baseline score.

    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 action ('Request interactive feedback') and specifies the target ('for a given project directory and summary'), making the purpose understandable. However, it doesn't distinguish from siblings since none exist, and 'interactive feedback' could be more specific about what type of feedback is being requested.

    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, such as appropriate contexts, prerequisites, or alternatives. It merely states what the tool does without indicating scenarios where it should or shouldn't be invoked.

    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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  • Evaluate tool definition quality.

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