Skip to main content
Glama

netintel_translate

Translate up to 2,000 words between 30+ languages, auto-detecting the source and preserving formatting for larger documents and articles.

Instructions

Translate up to 2000 words between languages using Claude Haiku — auto-detects source, supports 30+ target languages, preserves formatting, for larger documents and articles. For short snippets use the cheaper /translate/short.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
sourceNo
targetYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.61

TDQS

A4.4/5.0
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 it does disclose meaningful traits: a 2000-word cap, the underlying model (Claude Haiku), automatic source detection, 30+ target languages, and formatting preservation. It stops short of covering rate limits, cost magnitude, or what happens to input exceeding the cap.

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?

One tightly packed sentence followed by a routing clause. Every phrase earns its place: limit, model, capabilities, and the alternative are all front-loaded with zero filler.

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 translation tool with no output schema and no annotations, the description covers limits, model, auto-detection, formatting preservation, and sibling routing. The main gap is target/source language format, which an agent needs to construct a valid 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 description coverage is 0%, so the description must compensate. It clarifies that source is auto-detected (implying it is optional) and that target accepts 30+ languages, but it never states the expected format for 'target' (language code vs. name) or the optional 'source' format, leaving real ambiguity.

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?

States a specific verb+resource (translate text between languages) and immediately bounds the scope (up to 2000 words, larger documents and articles), which cleanly separates it from the translate_short sibling. An agent can tell which translation tool to reach for without opening any schema.

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

Usage Guidelines5/5

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

Explicitly names the alternative and the condition that selects it: 'For short snippets use the cheaper /translate/short,' and 'for larger documents and articles' for this tool. This is a genuine when/when-not routing rule with a cost rationale, not just implied context.

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

Deploy Server

Other Tools