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Glama

post_translate_text

TRANSLATION — POST {text, target} and get the translation plus the detected source language. Any language pair; target as a name or ISO code ('spanish', 'de', 'ja'). Up to 8,000 chars per call; line breaks and markdown preserved; code, URLs, and proper names left alone. Optional {source} to pin the source language, {formality}: formal|informal. Fast cheap LLM under the hood; the x402 payment IS the auth. ($0.01 per call, paid via x402)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe text to translate, up to 8,000 characters
sourceNoOptional source language; auto-detected when omitted
targetYesTarget language — name or ISO code ('spanish', 'de', 'ja')
formalityNoformal or informal register (optional)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNo
usageNo
targetNo
translationNo
source_languageNodetected (or provided) source language

Schema Changelog

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

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

The description reveals behavioral traits beyond annotations: 'Fast cheap LLM under the hood; the x402 payment IS the auth. ($0.01 per call, paid via x402)' This adds transparency about cost and authentication. Also mentions character limit and preservation of formatting, code, etc.

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?

The description is concise with three sentences. The first sentence front-loads the core purpose, the second covers constraints and examples, and the third adds optionality and cost. No unnecessary words.

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

Completeness5/5

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

Given the tool has an output schema (thus return values are covered) and the description covers purpose, parameters, limitations, cost, and authentication, it is fully complete for an agent to select and invoke correctly.

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

Parameters5/5

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

Schema coverage is 100%, but description adds significant value by providing examples for target ('spanish', 'de', 'ja'), specifying the character limit for text, and explaining optional parameters (source, formality). This goes beyond the schema's descriptions.

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 starts with 'TRANSLATION' and clearly states the action: POST text and target to get translation plus detected source language. It distinguishes itself from sibling tools by being the only translation tool among many post_* tools.

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

Usage Guidelines4/5

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

The description provides explicit guidance on how to use the tool: any language pair, target as name or ISO code, examples given. It does not explicitly state when not to use it, but the context of a single translation tool makes it clear.

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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TDQS

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes, but the SEO-related tools (head_check, full_audit, site_audit, etc.) overlap in scope, potentially causing confusion despite clear descriptions.

Naming Consistency5/5

Tool names consistently follow a get_/post_/delete_ verb pattern with descriptive noun phrases (e.g., get_seo_head_check, post_store_collection), with no mixing of naming conventions.

Tool Count2/5

With 46 tools covering a wide breadth of domains (SEO, accessibility, music, crypto, linting, etc.), the count is excessive for a single server, feeling unfocused and heavy.

Completeness4/5

The tool set covers most core operations for each sub-domain, but minor gaps exist (e.g., missing update for datastore, limited music operations).