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Glama

fanar_translate

Translate between Arabic and English, including classical Arabic texts like hadith and fiqh. Use Fanar Shaheen models for up to about 4,000 words per request.

Instructions

Translate between Arabic and English with Fanar's Shaheen models, including classical Arabic such as hadith and fiqh texts. About 20 requests a day; keep each request under about 4,000 words.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
modelNoFanar-3.0-Shaheen-Translate
langpairYes
preprocessingNodefault

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden, and it does disclose genuinely useful operational traits: a ~20 requests/day quota and a ~4,000-word per-request limit. It stops short of describing return format, error behavior, or latency, and no output schema exists to cover that gap.

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?

Two tight sentences that front-load the capability statement and then the operational limits. Every clause carries information; nothing is restated from the name.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a four-parameter tool with zero schema description coverage, no annotations, and no output schema, the description should document the model and preprocessing choices and say something about the returned translation. It covers quotas and text length but leaves the parameter surface entirely unexplained.

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

Parameters2/5

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

Schema description coverage is 0% for four parameters, three of which are enums, so the schema offers no semantic help either. The description mentions the language pair only implicitly ('between Arabic and English') and says nothing about the model enum options or the preprocessing modes (preserve_html, preserve_whitespace, etc.), leaving the agent to guess.

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 (Translate) and resource (text between Arabic and English) plus the underlying model family, and adds a distinguishing scope note about classical Arabic (hadith, fiqh). This clearly separates it from siblings like fanar_chat, fanar_islamic_qa, and fanar_poem.

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 the intended use case (Arabic/English translation, including classical religious texts) and gives operating constraints, but never states when to prefer this over fanar_chat or other siblings, nor any exclusions or prerequisites. Usage is inferable but not routed.

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