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luochang212

Card Magic MCP

by luochang212
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.9

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one encodes information into a card ordering, the other decodes it. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow the same verb_noun_tool pattern consistently (decode_cards_tool and encode_cards_tool).

    Tool Count4/5

    Two tools is slightly below the typical 3-15 range for a well-scoped server, but for a very focused domain (a specific card trick), it is still reasonable and each tool earns its place.

    Completeness5/5

    The tool set covers the complete workflow for the card trick: encoding (arranging five cards to hide the fifth) and decoding (recovering the fifth card from the arrangement). There are no missing operations.

  • Average 3.2/5 across 2 of 2 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 is passing

  • 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, and the description only states the magic trick operation. It does not disclose any behavioral traits, such as whether it is deterministic, error handling, or if it modifies state.

    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?

    Single sentence capturing the essence. No wasted words, though it is in Chinese. It could be slightly more informative but maintains conciseness.

    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?

    The tool has an output schema but the description never mentions what the function returns. For a guessing tool, the output is implicit but not stated. Given the simplicity, it is still incomplete.

    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 input schema has 100% coverage with a detailed description of the 'cards' parameter format. The description itself adds no extra meaning beyond the schema, so baseline score of 3 is appropriate.

    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 it implements a magician's trick to guess the fifth card from four given cards. It uses specific verb and resource, though it doesn't distinguish from sibling tool encode_cards_tool (likely the reverse operation).

    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 the sibling tool. It lacks any context about appropriate usage scenarios or prerequisites.

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

  • Behavior3/5

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

    The description explains the core encoding algorithm, which adds transparency. However, it does not disclose side effects, permissions, or error conditions. Without annotations, the description carries the burden, and while it explains what the tool does, it omits details like return value or idempotency.

    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 a single sentence that efficiently conveys the tool's purpose without unnecessary words. It is front-loaded with the key concept (magician Chico's operation) and is structurally sound, though the sentence is somewhat long.

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

    Completeness3/5

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

    Given the tool's complexity (a specific card trick), the description partially explains the logic but assumes knowledge of 'magician Chico'. The output schema is present but not described. The sibling tool 'decode_cards_tool' is listed, providing some context, but the description itself is not fully self-contained.

    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 already provides a detailed description of the 'cards' parameter including format, suit, and rank options. The tool description adds no additional semantic information beyond the schema. Since schema coverage is 100%, a baseline score of 3 is appropriate.

    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 tool's function: reordering five cards and encoding the fifth card's information into the order of the first four. It uses a specific verb ('implements', 'reorder', 'encode') and identifies the resource (cards). It also distinguishes from its sibling tool 'decode_cards_tool' by context.

    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 does not provide any guidance on when to use this tool versus alternatives (e.g., when to encode vs. decode). No prerequisites or prohibitions are mentioned, leaving the agent to infer usage solely from the tool name and sibling.

    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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