Skip to main content
Glama

Translate Batch

translate_batch

Translates up to 50 texts in a single call, using batched Google requests or AI per text, and auto-detects source language when needed.

Instructions

Translate up to 50 texts in one call (one batched Google request, or one AI call per text).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textsYesThe texts to translate, in order.
engineNoTranslation engine override: google (free) or ai (BYOK).
source_langNoSource language code, or "auto" to detect. Defaults to auto.
target_langNoTarget language code (e.g. en, zh-CN, ja). Defaults to the server config.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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 a meaningful behavioral trait: the engine-dependent call pattern ('one batched Google request, or one AI call per text'), which has latency/cost implications. However it says nothing about per-text failure handling, partial results, result ordering, or BYOK/auth requirements for the ai engine.

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?

A single sentence with the core batch capability front-loaded and a compact parenthetical for the engine nuance. Nothing is redundant and the size matches the tool's simplicity.

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 4-parameter batch tool with no annotations, no output schema, and a 50-item input, the description is adequate but thin: it omits how partial failures or ordering are handled and what auth the ai engine needs. The description need not cover return values, but batch failure semantics are a real gap.

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 100%, so the schema already documents texts, engine, source_lang, and target_lang; baseline of 3 applies. The description reinforces the 50-item cap and hints that the engine choice changes call behavior, but adds no format or default details beyond the schema.

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 ('Translate ... texts') plus a concrete capacity ceiling ('up to 50 texts in one call'), which distinguishes it from the singular translate_text sibling. It stops short of naming the sibling or contrasting directly against it, so differentiation is inferable rather than explicit.

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 'up to 50 texts in one call' phrasing implies this is the batch alternative to translate_text, giving usable but implicit guidance. No explicit when-to-use, when-not-to-use, or reference to translate_text / list_engines is provided, so the agent must infer the routing.

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