Detect Translation Errors

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

Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, covering safety and idempotency. The description adds valuable context beyond this: it specifies the tool 'focuses on error detection, providing detailed information about translation errors with their severity levels and positions', which helps the agent understand the granularity and output structure. No contradictions with annotations exist.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is well-structured and front-loaded: it starts with a clear purpose statement, then details parameters and returns, and ends with usage examples. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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

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

Given the tool's complexity (error detection with severity levels), rich annotations, 100% schema coverage, and the presence of an output schema that fully documents return values, the description is complete enough. It covers purpose, parameters, returns, and usage examples, leaving no gaps for the agent to understand and invoke the tool correctly.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description lists parameters (source, translation, reference, min_severity, response_format) and adds minimal context like 'optional' for reference and default values, but does not provide significant additional meaning beyond what the schema offers. The baseline of 3 is appropriate given high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Detect and categorize errors in a translation' with specific verbs ('detect', 'categorize') and resource ('translation'). It distinguishes from siblings by focusing on error detection rather than evaluation or batch processing, as indicated by the sibling tools xcomet_batch_evaluate and xcomet_evaluate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use the tool through examples: 'Find critical errors before publication', 'Identify areas needing post-editing', 'Quality gate for MT output'. However, it does not explicitly state when NOT to use it or mention alternatives like the sibling tools, which would be needed for a score of 5.

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