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Freely-Given-org

Open English Translation of the Bible

Official

compare_translations

Compares OET-RV and OET-LV for a given reference, detailing added words, untranslated particles, explicit translation decisions, and structural differences to expose translation divergencies.

Instructions

Isolates and computes detailed translation divergencies between OET-RV and OET-LV for a passage.

Identifies:

  • Added words in LV (copulas, articles, direct objects).

  • Untranslated original language particles (Greek articles before proper names, Hebrew DOM).

  • Explicit RV translation decisions (referents substituted for pronouns, rewordings, number changes).

  • Poetic parallelism (≈) and clause inversions (⇔).

Args: reference: Scripture reference (e.g. 'John 1:1-3', 'Romans 1:16-17').

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
referenceYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.1

TDQS

A3.9/5.0
Behavior4/5

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

The description lists concrete outputs: added words, untranslated particles, explicit translation decisions, poetic parallelism, and clause inversions. This gives an agent a clear expectation of the tool's behavior and result format. No side effects are mentioned, but the tool appears to be read-only, consistent with the absence of annotations.

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

Conciseness4/5

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

The description is moderately detailed but well-organized with a bulleted list of identification categories. It is not overly verbose and communicates the core purpose and expected output efficiently. The structure makes the information easy to parse.

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?

Given the presence of an output schema (not shown but indicated), the description provides sufficient context about the tool's function and output. It does not explicitly explain its relationship to sibling tools, but the listing of sibling tools helps an agent infer when to use this one. No critical information appears to be missing for a typical use case.

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?

The only parameter, 'reference', has no schema description (0% coverage), but the tool description provides examples like 'John 1:1-3' and 'Romans 1:16-17', which clarifies the expected format. This adds enough meaning to compensate for the bare schema, though it is a common parameter type that an agent might infer.

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?

The description clearly states the tool's function: computing detailed translation divergencies between OET-RV and OET-LV for a passage. The verb 'isolates and computes' is specific, and the resource (translation versions) is named. It distinguishes itself from siblings like get_passage and get_translation_decisions by focusing on comparison rather than retrieval or decision lookup.

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 description implies when to use the tool (when detailed divergencies are needed) but does not explicitly contrast it with alternatives like get_translation_decisions, which might also be relevant. No clear 'when not to use' guidance is provided, leaving some ambiguity for an agent deciding between closely related tools.

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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