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shuji-bonji

xcomet-mcp-server

by shuji-bonji
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TypeScript

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.3.5

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: batch evaluation, error detection, and single pair evaluation. There is no overlap; an agent can easily select the appropriate tool based on whether it needs aggregate stats, detailed error spans, or a single quality score.

    Naming Consistency5/5

    All tool names follow the consistent pattern 'xcomet_<verb>' in snake_case, making it easy to predict tool functionality from the name. No mixing of conventions or irregular naming.

    Tool Count4/5

    With 3 tools, the server is on the lower end of typical scope but still covers the core evaluation workflow. It is not unreasonably sparse; adding a few more tools (e.g., model info, comparison) would improve completeness.

    Completeness4/5

    The tools cover single evaluation, batch evaluation, and detailed error detection, which together address the primary use cases for translation quality assessment. Minor gaps exist, such as no tool for listing models or configuring settings, but the core functionality is present.

  • Average 4.5/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 4 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.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

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

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

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

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

  • Behavior4/5

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

    Adds value beyond annotations by detailing GPU usage, batch size limits, and memory implications; no contradictions.

    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?

    Well-structured with sections, front-loaded purpose, but somewhat verbose; could trim repetitive parameter details.

    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?

    Fully covers all aspects given 6 params, output schema, and annotations; no gaps in usage or behavior.

    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 already covers parameters well; description adds extra context like batch size range and examples, going 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?

    Clearly states the tool evaluates multiple translation pairs in a batch, distinguishing it from single-pair evaluate and error detection siblings.

    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?

    Provides examples and context but lacks explicit comparison to alternatives for when to choose batch vs single evaluate or detect-errors.

    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?

    Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, which the description does not contradict. The description adds valuable behavioral context such as the return structure (errors with severity and positions) and GPU inference capability, going beyond the annotations.

    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 (purpose, args, returns, examples) and front-loaded with the main action. It is appropriately sized for the complexity of the tool, with no wasted sentences.

    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 has 6 parameters, 2 required, enums, and no output schema in the input schema, the description provides complete context: behavior, parameters, output structure, and examples. Everything an agent needs to select and invoke the tool correctly is present.

    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 100% description coverage, so baseline is 3. The description adds meaning by listing parameters with defaults and enum values, and more importantly, provides a detailed output schema in the Returns section, which is not present in the input schema. This helps the agent understand what the tool returns.

    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 detects and categorizes translation errors, with a specific verb and resource. It distinguishes itself from sibling tools by focusing on error detection rather than batch evaluation or overall quality scoring.

    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 provides explicit usage examples (e.g., 'Find critical errors before publication', 'Quality gate for MT output'), indicating when to use the tool. However, it does not explicitly state when not to use it or compare directly with sibling tools for alternative scenarios.

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

  • Behavior5/5

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

    Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds that it uses xCOMET model, returns quality scores, error spans (with severity), and a summary. No contradictions; behavior is fully disclosed with no hidden 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.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is well-structured with a lead sentence, bullet points for outputs, labeled Args list, Returns section with JSON format, and usage examples. It is slightly verbose but each sentence serves a purpose, making it efficient.

    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 7 parameters (2 required), 100% schema coverage, presence of output schema, and sibling tools, the description is complete: describes all parameters, return structure, usage examples, and model name. No gaps for agent decision-making.

    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 description coverage is 100%, so baseline is 3. The description adds value by providing more context for each parameter (e.g., 'source: Original source text to translate from') and showing the return format with example output. This goes beyond schema terseness.

    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 evaluates translation quality using xCOMET model. It lists specific outputs (score, errors, summary) and examples. It distinguishes from siblings (xcomet_batch_evaluate for batch, xcomet_detect_errors for error-only) by focusing on single pair evaluation with full output.

    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 provides usage examples (evaluate EN→JA, check post-editing need, compare against reference) which imply when to use. It does not explicitly state when not to use or mention alternatives, but sibling tools are known. Still, clear context for single evaluation.

    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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Glama performs regular codebase and documentation scans to:

  • 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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