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doy00

TOEIC Speaking MCP Server

by doy00
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JavaScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct roles: one generates a quiz question, the other validates the user's answer. There is no functional overlap between them.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern: 'get_quiz_sentence' and 'verify_answer'. This predictable naming makes the tool set easy to navigate.

    Tool Count3/5

    With only 2 tools, the server is on the thin side, but the narrow quiz-taking purpose makes this count borderline acceptable. It covers the essential actions without being bloated.

    Completeness4/5

    The tools form a complete core workflow: retrieve a question and verify a response. Minor gaps exist, such as no explicit score retrieval or session management, but the primary quiz loop is fully covered.

  • Average 3.4/5 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 status not available

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  • 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?

    With no annotations provided, the description carries the full burden of disclosure. It does mention the prioritization behavior (low mastery_score first), but it does not explain what the tool returns (e.g., a single sentence or a list), how many items are generated, or whether any state changes occur. This is a significant gap for a generation tool.

    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 primary purpose. Every word earns its place, with no redundant filler.

    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?

    The tool is relatively simple with two optional parameters and no output schema, so the description is adequate for basic understanding. However, it omits return value details (what exactly the tool returns) and lacks explicit connection to the sibling verify_answer tool, leaving some context 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?

    Schema description coverage is 100%, with both parameters ('mode' and 'part') already documented in the schema. The description adds no additional meaning beyond the schema, so the 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 uses a specific verb ('출제' = generate/select questions), names the resource ('quiz sentence'), and includes a key detail (prioritizing low mastery_score). This clearly distinguishes it from the sibling tool verify_answer, which verifies answers rather than generating questions.

    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 guidance is given on when to use this tool versus verify_answer, nor when to choose the 'weak_first' or 'random' modes. The description implies usage for quiz generation but does not provide explicit context or exclusions.

    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?

    With no annotations, the description carries the full burden of behavioral disclosure. It discloses that the tool updates scores based on keyword inclusion, which is useful context, but it does not explain side effects, irreversibility, permission requirements, or return values. The description gives partial transparency but leaves important gaps.

    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 concise sentence that directly conveys the tool's purpose and core behavior. No wasted words or redundant details.

    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?

    The tool is relatively simple with two parameters and no output schema, but the description does not mention what the tool returns or what 'updates the score' entails. It provides the core action but lacks context about the response and any prerequisites, leaving the description minimally sufficient rather than complete.

    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?

    Schema description coverage is 100%, and the description does not add meaningful information beyond the parameter descriptions already present. The baseline of 3 applies because the schema handles parameter semantics adequately.

    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 a specific action: verifying the user's answer and updating the score based on keyword inclusion. It distinguishes the tool from its sibling 'get_quiz_sentence' by focusing on verification and score update rather than retrieval.

    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 is provided on when to use this tool versus alternatives. The description implies it is used for answer verification and scoring, but does not state prerequisites, exclusions, or when a different tool would be more appropriate.

    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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