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xiaobenyang-com

Chess

by xiaobenyang-com
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TypeScript

Server Quality Checklist

67%
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 confusion or overlap between tools, as there are no other tools to compare it to. The tool's purpose is clearly defined and standalone.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect with no deviations or mixed conventions to evaluate. The tool name follows a clear verb_noun pattern.

    Tool Count2/5

    A single tool for a server named 'Chess' is too few for the apparent scope, as chess involves many operations like moves, game states, and analysis. This feels thin and incomplete for the domain.

    Completeness1/5

    The tool set is severely incomplete for chess, covering only FEN visualization without any operations for playing, moving pieces, checking game status, or other core chess functionalities. This will cause significant agent failures.

  • Average 3.3/5 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 Apache 2.0.

  • 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. It states the conversion function but lacks behavioral details such as error handling for invalid FEN, output format specifics (e.g., line breaks, character set), or performance considerations. This leaves gaps in understanding how the tool behaves beyond its basic purpose.

    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, efficient sentence that front-loads the core functionality without any wasted words. It is appropriately sized for the tool's straightforward purpose.

    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 tool's complexity (simple conversion), no annotations, no output schema, and low parameter coverage, the description is incomplete. It lacks details on output format, error cases, and parameter semantics, making it insufficient for reliable agent use without additional context.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, and the description does not add meaning beyond the schema. It mentions 'FEN notation' but does not explain what FEN is, valid formats, or examples. With 1 undocumented parameter, the description fails to compensate for the schema's lack of details.

    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 ('Convert') and target ('FEN notation to ASCII chess board visualization'), providing a precise verb+resource combination. It distinguishes the tool's purpose effectively without relying on sibling tools for context.

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

    Usage Guidelines3/5

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

    The description implies usage when FEN notation needs visualization, but provides no explicit guidance on when to use this tool versus alternatives or any exclusions. With no sibling tools, the context is straightforward but lacks detailed operational boundaries.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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