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zxw94FE

Prompt Optimizer MCP Server

by zxw94FE

Server Quality Checklist

58%
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 with other tools. The tool's purpose is clearly defined as optimizing prompts for better AI interactions, making it distinct by default.

    Naming Consistency5/5

    The single tool name 'optimize_prompt' follows a consistent verb_noun pattern (optimize + prompt). Since there is only one tool, naming consistency is inherently perfect with no deviations to assess.

    Tool Count2/5

    A single tool is too few for a server named 'Prompt Optimizer MCP Server', which suggests a broader scope of prompt-related operations. The tool count feels thin and incomplete for the implied domain, lacking basic functionalities like analyzing, testing, or comparing prompts.

    Completeness2/5

    The server is severely incomplete for prompt optimization. It only provides optimization but misses essential operations such as analyzing prompt effectiveness, generating variations, testing prompts, or managing prompt history, leaving significant gaps in the workflow.

  • 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
  • This repository is licensed under MIT License.

  • 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 states the tool analyzes prompts and applies optimization strategies, but it doesn't describe what the optimization entails (e.g., whether it modifies the prompt in-place, returns suggestions, or requires user confirmation), potential side effects, or any constraints like rate limits or authentication needs. This is a significant gap for a tool with no annotation coverage.

    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 concise and front-loaded, stating the core purpose in the first sentence and elaborating briefly in the second. Both sentences earn their place by defining the tool's function and scope. It could be slightly more structured (e.g., by mentioning output), but it avoids redundancy and is appropriately sized.

    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 (5 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the tool returns (e.g., an optimized prompt, a list of suggestions, or a score), how optimizations are applied, or any behavioral traits. For a tool with no structured fields to rely on, this leaves too much ambiguity for effective agent use.

    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%, so the schema already documents all 5 parameters thoroughly. The description adds no additional meaning beyond what the schema provides—it doesn't explain parameter interactions, default behaviors, or examples. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't 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: '优化和增强提示词以获得更好的 AI 交互效果' (optimize and enhance prompts for better AI interaction). It specifies the action (analyze prompts and apply optimization strategies) and the resource (prompts), though it doesn't differentiate from siblings since none exist. The purpose is clear but could be more specific about the output format.

    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 explicit guidance on when to use this tool versus alternatives is provided. The description mentions analyzing prompts and applying optimization strategies, but it doesn't specify prerequisites, ideal scenarios, or limitations. Without sibling tools, this is less critical, but the lack of any usage context leaves a gap.

    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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