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guangxiangdebizi

Quant2Ptrader-MCP

by guangxiangdebizi
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.2.0

  • Disambiguation5/5

    With only one tool, there is no ambiguity or overlap with other tools. The tool has a single, clear purpose: converting JoinQuant strategy code to Ptrade format, making it impossible for an agent to misselect between tools.

    Naming Consistency5/5

    The tool name 'convert_joinquant_to_ptrade' follows a consistent verb_noun pattern, using snake_case and clearly describing the action (convert) and target (JoinQuant to Ptrade). Since there is only one tool, consistency is inherently perfect.

    Tool Count2/5

    A single tool is too few for a server named 'Quant2Ptrader-MCP', which suggests a broader scope for quantitative trading strategy conversion. While the tool is well-defined, the set lacks related operations like validation, testing, or reverse conversion, making it feel incomplete for the implied domain.

    Completeness2/5

    The tool surface is severely incomplete for the server's apparent purpose of converting between trading platforms. There are significant gaps, such as no tools for validating converted code, handling errors, or supporting other conversion directions (e.g., Ptrade to JoinQuant), which could lead to agent failures in real-world workflows.

  • Average 4.2/5 across 1 of 1 tools scored.

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

    • 0 of 1 community issues answered or closed 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 Apache 2.0.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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

  • Behavior3/5

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

    With no annotations provided, the description carries the full disclosure burden. It adequately explains the processing behavior ('自动识别并转换所有API调用') and output artifacts ('生成Ptrade可用的.py文件和详细转换报告'), but omits critical operational details like file overwrite behavior, error handling, or whether the conversion is destructive.

    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 single-sentence structure is densely packed with zero waste: purpose clause → input options → processing logic → output specification. Information is front-loaded with the conversion action immediately stated.

    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 three parameters with complete schema coverage and no output schema, the description appropriately compensates by detailing the generated artifacts (conversion report and .py file). It could be improved by mentioning error conditions or prerequisites, but adequately covers the core workflow.

    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?

    Despite 100% schema coverage, the description adds valuable context: it clarifies the XOR relationship between code and filepath, specifies supported extensions (.py/.txt), and explains the default behavior for output_dir. This goes beyond mere schema repetition.

    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 specific action (转换/convert), source format (聚宽/JoinQuant), and target format (Ptrade), with no ambiguity. It effectively communicates the tool's singular purpose without needing sibling differentiation.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description clarifies the input mutual exclusivity ('可以直接输入代码字符串，或提供文件路径') and supported file formats ('支持.py/.txt等格式'). While it lacks explicit 'when not to use' guidance, this is acceptable given no sibling tools exist for alternative conversion paths.

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

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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