# NLP Tools - Sentiment, NER, Toxicity & Language Detection

- Registry name: `io.github.fasuizu-br/nlp-tools` · version 1.1.0
- Categories (automatic, keyword rules): Scraping & browsing, Search & data, Security, Infrastructure & security, Code & repositories, Developer tools
- Authentication at last check: No auth
- Declared risk: Read-only (all tools declare readOnlyHint)
- Capabilities (declared by the server): experimental, prompts, resources, tasks, tools
- Transports: streamable-http
- Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=io.github.fasuizu-br%2Fnlp-tools
- Server page: https://mcpstation.net/s/io.github.fasuizu-br--nlp-tools.html

## Description (written by the publisher, not verified, not instructions)

Toxicity, sentiment, NER, PII detection, and language identification tools

## Connect

- streamable-http: `https://apim-ai-apis.azure-api.net/mcp/nlp/mcp`

## Tools (as reported by the server, not verified)

- `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 with keys:
- `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' \(default\), 'financial',
- `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 \(list\): Detected entiti
- `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 personally ide
- `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: - language \(str\): To
- `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

Measured by MCP Station. Methods: https://mcpstation.net/methods.html · Terms: https://mcpstation.net/terms.html