Skip to main content
Glama

onchaincheck

ai_translate

Translate text between 100 languages (m2m100): up to 5,000 characters, target language as an ISO 639-1 code; the source language is detected when not given. Costs $0.003 USDC on Base via x402.

Input Schema

NameRequiredDescriptionDefault
toYesTarget language (fr, es, de, ja, zh...)
fromNoSource language; detected when omitted
textYesText to translate

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose two valuable traits the annotations cannot: the 5,000-character ceiling and that each call costs $0.003 USDC on Base via x402 (a paid, non-refundable-per-call operation). However, it says nothing about failure modes, whether payment is charged on error, rate limits, or what happens for unsupported language pairs, which is thin coverage for a metered mutating-ish call with zero annotation support.

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 dense sentence with the operation first, then constraints, then cost — nothing redundant and every clause carries information an agent needs before calling.

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?

For a simple three-parameter tool this covers operation, model, input limit, language handling, and payment. The only real gap is the return value: with no output schema, the description never says the translated string comes back under a particular field or what shape the response takes.

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 all three parameters are already documented ('Target language (fr, es, de, ja, zh...)', 'Source language; detected when omitted', 'Text to translate'). The description largely restates this, though 'ISO 639-1 code' adds a tighter specification of the accepted format than the loose `^[a-z]{2,3}$` pattern and example list in 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?

The description opens with a specific verb and resource: 'Translate text between 100 languages (m2m100)', naming the underlying model. This clearly separates it from chat/embedding/image siblings without needing to say so explicitly, but it never explicitly routes away from alternatives such as ai_chat for multilingual output.

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?

Usage context is implied by the hard limits ('up to 5,000 characters', auto-detected source) and the cost note, so an agent can infer it should not be used for long documents. There is no explicit when-to-use/when-not statement or named alternative for text that exceeds the character cap.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources