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Detect Translation Errors

xcomet_detect_errors
Read-onlyIdempotent

Analyze translations to detect and categorize errors by severity, providing detailed positions and suggestions for improvement.

Instructions

Detect and categorize errors in a translation.

This tool focuses on error detection, providing detailed information about translation errors with their severity levels and positions.

Args:

  • source (string): Original source text

  • translation (string): Translated text to analyze

  • reference (string, optional): Reference translation

  • min_severity ('minor' | 'major' | 'critical'): Minimum severity to report (default: 'minor')

  • response_format ('json' | 'markdown'): Output format (default: 'json')

  • use_gpu (boolean, optional): Use GPU for inference if available (default: false)

Returns: { "total_errors": number, "errors_by_severity": { "minor": number, "major": number, "critical": number }, "errors": [ { "text": string, "start": number, "end": number, "severity": "minor" | "major" | "critical", "suggestion": string | null } ] }

Examples:

  • Find critical errors before publication

  • Identify areas needing post-editing

  • Quality gate for MT output

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYesOriginal source text
translationYesTranslated text to analyze
referenceNoOptional reference translation
min_severityNoMinimum severity level to report (minor, major, critical)minor
response_formatNoOutput formatjson
use_gpuNoUse GPU for inference (faster if available). Default: false (CPU only)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
total_errorsYesTotal number of errors detected
errors_by_severityYesError count by severity
errorsYesDetailed error list
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.

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