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Thamindu-Dev

sinhala-mcp

by Thamindu-Dev
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.8

  • Disambiguation5/5

    Health check and translation are completely distinct concerns with no functional overlap. An agent can easily choose the correct tool.

    Naming Consistency3/5

    Both tools use snake_case, but 'health_check' is a noun phrase while 'translate_sinhala_instruction' is imperative verb+noun, showing minor inconsistency.

    Tool Count3/5

    With only two tools, the set is minimal but arguably sufficient for the narrow scope of health checking and translation. Borderline acceptable.

    Completeness3/5

    The server covers only one main operation (translation) plus health check. Missing auxiliary features like language detection or reverse translation, but core purpose is served.

  • Average 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
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • 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.

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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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 must carry the full burden. It fails to disclose behavioral traits such as side effects (likely none), rate limits, or what happens on failure. The description is minimal and lacks depth.

    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?

    Single sentence, no wasted words. Every word earns its place. Appropriately sized for the tool's simplicity.

    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 description is adequate for a simple health check, but lacks mention of return value or status format. Since no output schema exists, some detail on expected output would improve completeness.

    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?

    No parameters exist (input schema empty with 100% schema coverage). Baseline for 0 parameters is 4. Description adds no parameter info as there are none.

    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?

    Description clearly states the tool checks health of MCP server and Gemini API connection, using a specific verb ('Check') and explicit resources. It distinctly differs from sibling tool 'translate_sinhala_instruction' which is unrelated.

    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?

    No explicit guidance on when to use or avoid this tool. The purpose is implied (diagnostics) but no alternatives or context are provided. With only one sibling, no comparison is made.

    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?

    No annotations are provided, so the description carries the full burden. It states the transformation and automatic context inference, but it does not mention input validation behavior (e.g., if input is not Sinhala/Singlish) or output format. The description is adequate but lacks details about potential limitations or side effects.

    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 two sentences, no wasted words, and front-loads the main action. Every sentence adds value.

    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?

    For a simple translation tool with one parameter and no output schema, the description is relatively complete. It covers the input, transformation, and output context. However, it lacks details about error handling or behavior on invalid input, which would improve completeness.

    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 already describes the parameter as a Sinhala/Singlish instruction with a max length. The description adds meaning by specifying the output: a precise English technical prompt for an AI coding agent, and mentions automatic context inference, which provides additional context 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 verb ('Translate'), resource ('Sinhala or Singlish instruction'), and target output ('precise English technical prompt for an AI coding agent'). It also mentions automatic context inference, distinguishing it from its only sibling, health_check.

    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 purpose is clear enough that an agent would know to use this tool when it has a non-English instruction for the AI agent. However, it does not explicitly state when to use or not use it (e.g., if the input is already English), nor does it provide alternatives. Given the sibling is health_check, the need is lower, but still a minor gap.

    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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