Skip to main content
Glama

Deepl

by io.github.pipeworx-io

Translate

translate
Read-onlyIdempotent

Translate one or more text strings using DeepL. Requires text (string or array) and target_lang (e.g. "DE", "FR", "JA"). Optionally specify source_lang, formality (more/less/prefer_more/prefer_less), tag_handling (html/xml), and glossary_id. Returns detected source language and translated text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesString or array of strings.
formalityNo
glossary_idNo
ignore_tagsNo
source_langNo
target_langYes
tag_handlingNo
splitting_tagsNo
split_sentencesNo
outline_detectionNo
non_splitting_tagsNo
preserve_formattingNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
translationsYesArray of translated text results

Schema Changelog

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

  1. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "target_lang": "DE",
+    "text": "Hello, how are you?"
+  },
+  {
+    "formality": "formal",
+    "source_lang": "EN",
+    "target_lang": "FR",
+    "text": [
+      "Good morning",
+      "Good evening"
+    ]
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "translations": {
+      "description": "Array of translated text results",
+      "items": {
+        "properties": {
+          "detected_source_language": {
+            "description": "Detected source language code",
+            "type": "string"
+          },
+          "text": {
+            "description": "Translated text",
+            "type": "string"
+          }
+        },
+        "required": [
+          "text"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "translations"
+  ],
+  "type": "object"
+}
  2. First observed

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already declare the tool as read-only, idempotent, and non-destructive, which covers the safety profile. The description adds return value details and parameter behaviors, but does not disclose additional traits like rate limits or error handling. This is acceptable but not exceptionally transparent.

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 two sentences each earning their place. It front-loads the main function and then packs requirements and options efficiently.

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?

Given 12 parameters and low schema coverage, the description handles the core workflow well but omits several optional parameters. The output schema covers return values, so that aspect is complete, but the full parameter space is not fully explained.

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?

With 8% schema coverage, the description compensates by explaining core parameters (text, target_lang, formality, etc.) and providing examples. However, many advanced parameters (ignore_tags, splitting_tags, preserve_formatting) are left undocumented, creating a gap for agents.

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 specifies the action ('Translate') and the resource ('one or more text strings using DeepL'), distinguishing it from sibling tools like source_languages/target_languages which list languages rather than translate. It is specific and unambiguous.

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 clear context for when to use the tool (translating text) and outlines required and optional parameters. However, it does not offer explicit alternatives or when-not-to-use guidance, so it stops short of a 5.

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

All Deepl toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.3/5.0
Disambiguation2/5

Many tools have overlapping functionality, such as ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded, which are essentially the same with minor differences. The polymarket_* family also has five tools with similar names and purposes, making it easy to select the wrong one despite detailed descriptions.

Naming Consistency2/5

Tool names mix verb-first patterns (ask, generate, list, remember) with noun-first patterns (entity_profile, polymarket_arbitrage), and include camelCase like ai_visibility_check. This inconsistent naming style makes the set feel arbitrary and harder to navigate.

Tool Count3/5

With 36 tools, the server exceeds the typical well-scoped range of 3-15. While the multi-purpose nature justifies a larger set, the presence of many near-duplicates (beta/grounded variants, multiple polymarket tools) inflates the count without proportional functional gain.

Completeness4/5

The toolset covers a wide array of domains including translation, entity resolution, financial data, prediction markets, memory, subscriptions, and AI visibility. It appears very comprehensive for its intended multi-purpose server, with no obvious major gaps in core capabilities.

Resources