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netintel_translate_short

Translate up to 500 words between 30+ languages, auto-detect source, preserve formatting, and return translation with detected source for content localization.

Instructions

Translate up to 500 words between languages using Claude Haiku — auto-detects source language, supports 30+ target languages, preserves formatting, and returns the translation with detected source so agents can localize short content…

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
sourceNo
targetYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.61

TDQS

A3.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and handles it reasonably: it discloses the engine (Claude Haiku), auto source detection, 30+ target languages, formatting preservation, and that the response includes the detected source. It stops short of rate limits, error behavior, or truncation handling for over-limit input.

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

Conciseness3/5

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

Front-loaded with the core action and organized as a single em-dash feature list, but the trailing ellipsis ('…') makes the description read as truncated/incomplete, and several listed capabilities could be compressed.

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?

For a three-parameter text utility with no output schema and no annotations, the description covers the return content ('the translation with detected source') and core behaviors, which is the most important gap filled. It is still incomplete on language-code format and failure modes, so an agent may need a trial call.

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

Parameters3/5

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

Schema coverage is 0%, so the description must compensate. It usefully explains that source is auto-detected (matching the schema's optional 'source'), which is real meaning beyond the schema, but it never specifies the accepted format for 'target' (ISO code vs. language name) or how 'text' is bounded, leaving the key ambiguity unresolved.

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?

States a specific verb and resource with a scope bound ('Translate up to 500 words between languages'), and adds the engine used (Claude Haiku). It implicitly differentiates itself from the heavier translate/batch/structured siblings via the 500-word limit, but never names them explicitly, so an agent must infer the boundary.

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?

The clause 'so agents can localize short content' and the 'up to 500 words' limit imply the right use case, and the word cap hints that batch/long-form siblings exist. However, it never states when NOT to use this tool or names the alternative for longer or multi-language input, leaving the routing decision to inference.

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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