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liyb-gz

tinci-mcp

by liyb-gz
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool serves a distinct purpose: converting text to Jyutping, finding rhyming characters, and analyzing tone patterns. No overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern using snake_case (get_jyutping, get_rhyming_characters, get_tone_pattern).

    Tool Count4/5

    With 3 tools, the set is focused but covers the core needs for Cantonese lyrics writing. It feels slightly minimal but still appropriate for its niche scope.

    Completeness4/5

    The tool set covers the essential operations for Cantonese phonetics in lyrics: pronunciation, rhyming, and tonal analysis. Minor gaps like character lookup exist but are not central.

  • Average 4.2/5 across 3 of 3 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

  • Behavior3/5

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

    No annotations provided, so the description carries full burden. It discloses the return format and example, but does not address error handling, character coverage limitations, or performance. For a simple read-like tool, this is adequate but not thorough.

    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 concise with no wasted words. It starts with the main action, followed by explanation and example. Every sentence adds value, and the structure is clear.

    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 the tool's simplicity (1 parameter, no output schema), the description is nearly complete. It covers purpose, parameter meaning, return format, and example. Minor gaps like error behavior or edge cases are acceptable for such a straightforward tool.

    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?

    The input schema has 0% description coverage for the only parameter 'text'. The description adds meaning by specifying 'Cantonese text to convert (Traditional or Simplified Chinese)', which clarifies the input beyond the schema's bare type definition.

    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 purpose: 'Convert Cantonese text to Jyutping romanization.' It explains what Jyutping is and provides an illustrative example. The tool is distinct from siblings (get_rhyming_characters, get_tone_pattern) as it covers conversion to romanization.

    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 on when to use this tool versus alternatives. While the purpose is clear, there is no explicit when-to-use or when-not-to-use context, nor mention of prerequisites or exclusions.

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

  • Behavior4/5

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

    No annotations, so description carries full burden. It discloses that it matches on final, explains tone filtering, systems, and override behavior. No destructive or permission info, but for a read tool this is sufficient.

    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 lengthy but well-structured with sections for purpose, args, returns, and examples. Nearly every sentence adds value given the complexity of tone filtering and grouping.

    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?

    Covers all parameters, return format, and provides multiple examples. No output schema but description includes detailed return keys. Could mention error handling or input validation, but overall complete for a read tool.

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

    Parameters5/5

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

    Schema coverage is 0%, so description explains all 6 parameters in detail, including enums, defaults, and override relationships. Examples clarify usage. This adds significant value beyond schema.

    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 finds characters that rhyme with the input character for lyrics composition, based on Jyutping final. It distinguishes from sibling tools (get_jyutping, get_tone_pattern) by focusing on rhyming.

    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 implies usage for rhyming needs and provides examples. It does not explicitly contrast with siblings but context makes it clear. Slightly lacking explicit when-not-to-use guidance, but adequate.

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

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It details the tone grouping algorithms for both systems, the return structure (text, system, pattern, breakdown), and includes an example. It does not discuss error handling, performance, or limitations, but for a straightforward analysis tool, the transparency is adequate.

    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 well-structured with sections for purpose, system explanations, args, returns, and example. It is slightly lengthy but each section is justified. The bullet points for tone groupings are clear. Minor redundancy (e.g., repeating 'the X system uses different digits but same groupings') could be trimmed.

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

    Completeness5/5

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

    Given the tool's moderate complexity (2 parameters, no output schema), the description provides comprehensive information: input format, system choices with rationale, output structure, and a concrete example. It covers all necessary aspects for correct invocation and interpretation of results.

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

    Parameters5/5

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

    The input schema has 0% description coverage, but the description compensates fully. It explains both parameters: 'text' as Cantonese text to analyze, and 'system' as an enum with detailed explanations of each option (1056 and 0243), including the tone groupings. The example further clarifies usage. This adds significant value beyond the schema.

    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 purpose: analyzing tonal patterns of Cantonese text for lyrics writing. It specifies the verb ('analyze'), resource ('tonal pattern'), and domain ('Cantonese lyrics writing'). It distinguishes from siblings like get_jyutping (romanization) and get_rhyming_characters (rhyming) by focusing on tone mapping systems.

    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 implicitly provides usage context by explaining the two tonal classification systems (1056 and 0243) and their relevance to Cantonese lyrics. However, it does not explicitly state when to use this tool versus alternatives like get_jyutping or get_rhyming_characters, leaving the differentiation to the user's understanding of the tool's purpose.

    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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