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AstroWay Core astrology

Translate batch

astroway_content_localization_translate_batch
Read-onlyIdempotent

Translate up to 25 strings (≤2000 chars each, ≤20000 total) sharing one target language + domain. Credits: 15 + ceil(total_chars/40).

[Group: Content Localization] [Cost: 16 credits (Tier 1)]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYes
domainNo
fieldsNoCompact mode: comma-separated dotted paths to keep, relative to `data`, e.g. "planets.name,planets.longitude,houses.cusp". Omit for the whole response.
precisionNoCompact mode: round fractional numbers to this many decimals. Longitudes carry 14 by default; 2 is finer than any chart is drawn.
source_langNo
target_langYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
charsNo
countNo
itemsNo
domainNo
creditsNo
source_langNo
target_langNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description adds genuinely new behavioral context: per-string and per-batch size limits plus an explicit cost formula (15 + ceil(total_chars/40)), which is exactly the kind of cost/rate information annotations cannot carry.

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?

Front-loaded with the core constraints and cost in a single tight sentence; the group and cost tags are compact metadata lines. Every element earns its place with no filler.

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?

An output schema exists, so return values need not be explained. Still, for a 6-parameter tool the description omits source_lang entirely and gives no sibling routing, leaving notable gaps; it is adequate but not complete.

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 low (33%), so the description must compensate. It does add real meaning: items are bounded (≤25, ≤2000 chars, ≤20000 total) and must share one target language + domain. However, source_lang is never mentioned, and the compact-mode fields/precision parameters (only explained in the schema) sit oddly against a translation tool with no description-level context.

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?

The description states a specific verb and resource ('Translate up to 25 strings') and adds precise scope limits (2000 chars each, 20000 total, one shared target language + domain). It is clear and self-contained, but never distinguishes itself from the sibling translate_astro / translate_glossary_lang / translate_languages tools, which an agent selecting among them would want.

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

Usage Guidelines2/5

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

There is no when-to-use guidance. The batch/shared-domain framing implies bulk use, but nothing tells the agent when to pick this over the astro or glossary translation siblings, or what the single-string alternative is. The constraints imply a use case but leave selection 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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