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anzy-renlab-ai

Pronounce / pronounce-mcp

by anzy-renlab-ai
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Server Quality Checklist

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  • Latest release: v2.23.0

  • Disambiguation5/5

    Each tool has a distinct purpose: list_pronunciations lists entries with optional filters, while pronounce looks up a specific word's pronunciation. No functional overlap exists.

    Naming Consistency4/5

    Both tools use imperative verbs, but one is a compound verb_noun (list_pronunciations) and the other is a single verb (pronounce). This minor inconsistency does not hinder understanding.

    Tool Count5/5

    Two tools are appropriate for a pronunciation dictionary server: one for browsing/listings and one for detailed lookups. The scope is narrow and well-covered.

    Completeness5/5

    The server provides essential functionality for a read-only pronunciation resource: listing entries with filtering and retrieving detailed pronunciation data. No obvious gaps for its stated purpose.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • 1 of 1 community issues answered or closed in the last 6 months
    • 117 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • 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.

  • This repository includes a glama.json configuration file.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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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. The description discloses that the dictionary is large and growing, and that unfiltered lists are big. This adds some behavioral context, but could mention more about response structure or performance.

    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?

    Concise two-sentence description with clear bullet-like list of categories. Front-loaded with purpose. Could be slightly more structured but is efficient.

    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 output schema exists (but not shown), description covers param guidance. Lacks mention of authentication or pagination behavior beyond limit, but is sufficient for a list 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?

    Schema has 0% description coverage, but the description explains the purpose of category (listing possible values) and limit (keeping results focused), compensating for the lack of param descriptions. However, missing details like default limit value are already in schema.

    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 lists dictionary entries with optional category filtering. It distinguishes from siblings implicitly (list vs search), but does not explicitly contrast with 'search_pronunciations'.

    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?

    Provides advice to use category and limit for focused results, but does not specify when to use this tool over the sibling 'search_pronunciations' tool, nor when not to use it.

    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, the description fully discloses the return content (IPA, respelling, audio_url, alternate readings, source citation, editorial notes) and error behavior (error dict if word not found). It does not mention authentication or rate limits, but as a read-only lookup, this is acceptable.

    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 four sentences, each adding value. It is front-loaded with the main purpose, followed by return details, usage scope, and error handling. No superfluous information.

    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 simple input and no output schema, the description covers all essential aspects: purpose, return fields, error case, and scope limitations. It is sufficiently complete for an agent to use the tool correctly.

    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 only parameter 'word' is described implicitly through examples and scope (developer-related word). Schema coverage is 0%, so the description compensates by providing context that the word should be a tech term or acronym, which adds meaning 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 looks up canonical pronunciations for developer-related words, with specific examples (kubectl, nginx, GIF, etc.). It distinguishes from siblings like list_pronunciations and search_pronunciations by focusing on a single word lookup.

    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 explicitly says when to use the tool ('Use this for project names like...') and when not to ('for general English vocabulary, fall back to your own knowledge'). However, it does not compare directly to sibling tools, which could provide more precise guidance.

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

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