io.github.fasuizu-br/nlp-tools · v1.1.0 · communication
NLP Tools - Sentiment, NER, Toxicity & Language Detection is healthy: it completed an MCP handshake in 2618 ms and exposes 6 tools.
Toxicity, sentiment, NER, PII detection, and language identification tools
Endpoint: https://apim-ai-apis.azure-api.net/mcp/nlp/mcp (streamable-http)
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| Users (last 30 days) | 0 |
| Handshake | Healthy |
| Auth needed | None |
| Tool count | 6 |
| Latency | 2618 ms |
| Last checked (UTC) | 2026-10-15T03:50:36.687Z |
| Checked (UTC) | Status | Latency | Error |
|---|---|---|---|
| 2026-10-15T03:50:36.687Z | Healthy | 2618 |
analyze_toxicity | Analyze text for toxic content. Returns scores for 6 categories: toxic, severe_toxic, obscene, threat, insult, identity_hate. Each score is 0.0-1.0. BERT-based classifier with sub-15ms latency on GPU. Args: text: Text to analyze for toxicity (hate speech, insults, threats). Returns: dict |
analyze_sentiment | Analyze text sentiment. Returns positive/negative classification with confidence scores. DistilBERT-based with sub-10ms latency. Multiple domain-specific model variants available. Args: text: Text to analyze for sentiment (positive/negative). model: Model variant -- 'general' (default), 'f |
extract_entities | Extract named entities (NER) from text. Identifies persons, organizations, locations, and miscellaneous entities with span offsets and confidence scores. BERT-NER based with sub-50ms latency. Args: text: Text to extract named entities from. Returns: dict with keys: - entities (lis |
detect_pii | Detect personally identifiable information (PII) in text. Finds emails, phone numbers, SSNs, credit cards, IP addresses, and person names. Optionally returns redacted text with PII replaced by type labels (e.g. [EMAIL], [PHONE]). BERT-NER + regex ensemble. Args: text: Text to scan for personal |
detect_language | Detect the language of text. Supports 176 languages using fastText. Sub-1ms inference latency. Returns ISO 639-1 codes with confidence scores. Args: text: Text to identify the language of. top_k: Number of top language predictions to return (default: 3). Returns: dict with keys: |
check_nlp_service | Check health status of NLP API services and loaded models. Returns: dict with keys: - status (str): 'healthy' or error state - models (dict): Loaded model status per capability - version (str): API version |