Overview
Language Detector works by analyzing the text and comparing it to a database of languages and ai models. It uses advanced algorithms to detect the language of the text and returns the language code and the confidence level.
Live Test Language Detection for AI Agents Source →
The tool
Once your client is connected to the VerveContext server, this appears in its tool list as LanguageDetectionforAIAgents. It is read-only and open-world — it fetches and never mutates anything on your side — so most clients call it without asking you to confirm.
{
"name": "LanguageDetectionforAIAgents",
"arguments": {
"text": "Guten Tag, wie geht es Ihnen?"
}
}You do not name the tool yourself; the model picks it. Asking about Guten Tag, wie geht es Ihnen? in the terms this source covers is enough for it to reach for LanguageDetectionforAIAgents on its own — naming it explicitly also works, and is the way to force the call.
Connecting
One server URL covers every source in the catalog, including this one. Authorization is OAuth: the client opens a browser once, and there is no key to paste into a config file.
{
"mcpServers": {
"vervecontext": {
"url": "https://api.vervecontext.com/v1/mcp"
}
}
}https://api.vervecontext.com/v1/mcpPer-client setup — Claude, Cursor, VS Code, ChatGPT — is on the MCP setup page.
Arguments
These are the properties on the tool's inputSchema, so a well-behaved client validates them before the call is made. Premium arguments are accepted on every plan but only take effect on plans that include them.
| Argument | Type | Description |
|---|---|---|
textRequired | string | The text to detect the language of |
What the model gets back
The result carries a structuredContent object matching the tool's declared outputSchema, so a client reads fields without parsing prose. status is "ok" and error is null on success; a null field means the value was not available for that input, not that the call failed.
{
"status": "ok",
"error": null,
"data": {
"primaryLanguage": "spanish",
"primaryCode": "es",
"confidenceLevel": "medium",
"detectedLanguages": [
{
"language": "spanish",
"confidence": 0.38471794871794873,
"code": "es"
},
{
"language": "portuguese",
"confidence": 0.2946153846153846,
"code": "pt"
},
{
"language": "danish",
"confidence": 0.2464615384615384,
"code": "da"
}
]
}
}
Response fields
Paths are relative to data. Premium fields are absent rather than zeroed on plans that do not include them, so check for presence instead of comparing to 0.
| Field | Type | Example | Description |
|---|---|---|---|
primaryLanguage | string | spanish | The most likely detected language |
primaryCode | string | es | ISO 639-1 code of the primary language |
confidenceLevelPremium | string | medium | Overall confidence level: high, medium, or low |
detectedLanguages | array[3] | Every candidate language considered, ranked from most to least likely | |
detectedLanguages.0.language | string | spanish | The candidate language's full name |
detectedLanguages.0.confidence | number | 0.38471794871794873 | How strongly the sample matched this language's trigram frequencies, from 0 to 1 |
detectedLanguages.0.code | string | es | ISO 639-1 code of this candidate language |
Why ground on it
A model can produce something that looks like this answer from its training data, and be confidently out of date or simply wrong. This source returns the current value in a shape you can check, which is the difference between an answer you can cite and one you have to hedge.
Point an evaluation at primaryLanguage: it is the field most worth pinning a claim to, and it is either present and current or absent — never plausibly invented.
Failure modes
Errors come back as tool errors carrying a sentence the model can act on, not a bare status code. Error handling covers the full list.
| Status | What it means |
|---|---|
400 / 422 | The arguments did not validate. The message names the offending one. |
401 | The OAuth session is invalid or expired — reconnect the server. |
403 | Blocked by a key restriction or an IP allow-list. Never a bad identity. |
404 | This source is not part of VerveContext. Check the catalog. |
429 | Out of credits, or a brief rate limit. The message tells them apart. |
A call costs 2 credits each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.
Use cases
- Inbound Ticket Dispatch
- When an international customer submits a support ticket, help desks inspect the text to route the ticket to native-speaking agents.
- Voice Synthesizer Selection
- To read articles aloud with correct accents, screen readers check the language code before choosing a matching text-to-speech voice model.
- Search Stemmer Configuration
- Tag document uploads with ISO language codes during ingest so search engines apply the correct grammatical stemming rules for each language.
- Market Research Segmentation
- Analytics teams group raw global survey responses by primary language before running regional sentiment and feedback models.
Other ways to use Language Detection for AI Agents
Set up Language Detection for AI Agents on VerveContext, or reach the same source a different way. Your VerveContext account and credits work on all of them — one key, one balance.
Related
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