list_languagesList languages supported by the configured LibreTranslate instance.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Total number of supported languages | |
| languages | Yes | List of supported languages with codes and targets |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {}
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "count": {
+ "description": "Total number of supported languages",
+ "type": "integer"
+ },
+ "languages": {
+ "description": "List of supported languages with codes and targets",
+ "items": {
+ "properties": {
+ "code": {
+ "description": "Language code",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "name": {
+ "description": "Human-readable language name",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "target_codes": {
+ "description": "Language codes this language can be translated to",
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "count",
+ "languages"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds valuable context that the results depend on the 'configured LibreTranslate instance', indicating the list is dynamic rather than static. There is no contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that directly states the function without any unnecessary words or redundancy. It earns its place and is easily scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity, zero parameters, and the presence of an output schema, the description provides sufficient context for correct invocation. The annotations fill in safety and behavioral details, making the overall tool definition complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the input schema fully documents its input. The description does not need to elaborate on parameters, and the baseline of 4 applies because there is nothing to disambiguate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'List' and identifies the resource as 'languages supported by the configured LibreTranslate instance', which clearly distinguishes it from sibling tools like detect_language and translate. The purpose is unambiguous and self-contained.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: this tool retrieves the set of supported languages from the configured instance. While it does not explicitly name alternatives or state when not to use it, the tool's unique purpose among siblings makes the usage obvious and no exclusions are necessary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same underlying Pipeworx catalog, and the five polymarket_* tools all analyze prediction-market opportunities and edge. While the individual descriptions are detailed, an agent could easily select the wrong tool without deep reading, particularly between ask_pipeworx and its beta/variant versions.
The naming style is a mixture of imperative verb phrases (translate, validate_claim, forget, generate_llms_txt), noun phrases (entity_profile, recent_alerts, polymarket_arbitrage), and brand-prefixed nouns (ask_pipeworx, pipeworx_trending, bet_research). While all names are lowercase with underscores, there is no consistent verb_noun or domain-prefix convention across the toolset, making the API feel grab-bag rather than designed.
The server is named 'Libretranslate' — a translation service that needs only translate, detect_language, and list_languages — yet it exposes 34 tools spanning data research, prediction markets, memory storage, subscriptions, dependency scanning, AI-visibility probing, and llms.txt generation. This is an extreme scope mismatch: the overwhelming majority of tools serve completely unrelated functions that have nothing to do with the server's apparent purpose.
If judged purely as a translation server, the core surface is present but thin: translate, detect_language, and list_languages cover basic use, though there are no batch, format, or language-details options. If judged as the broader heterogeneous toolset, the domain is incoherent — no single workstream is fully covered, and the unrelated tools (Polymarket betting, Pipeworx research, memory, subscriptions) create a muddled surface with obvious gaps in any single stated purpose.