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

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

## Links
- Registry page: https://www.getdrio.com/mcp/io-github-fasuizu-br-nlp-tools
- Repository: https://github.com/fasuizu-br/speech-ai-examples
- Website: https://brainiall.com

## Install
- Endpoint: https://apim-ai-apis.azure-api.net/mcp/nlp/mcp
- Auth: Not captured

## Setup notes
- Remote endpoint: https://apim-ai-apis.azure-api.net/mcp/nlp/mcp

## Tools
- analyze_toxicity (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:
        - toxic (float 0-1): Overall toxicity score
        - severe_toxic (float 0-1): Severe toxicity score
        - obscene (float 0-1): Obscenity score
        - threat (float 0-1): Threat score
        - insult (float 0-1): Insult score
        - identity_hate (float 0-1): Identity-based hate score
        - is_toxic (bool): Whether text exceeds toxicity threshold Endpoint: https://apim-ai-apis.azure-api.net/mcp/nlp/mcp
- analyze_sentiment (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', 'twitter'.

Returns:
    dict with keys:
        - label (str): 'positive' or 'negative'
        - score (float 0-1): Confidence score for the predicted label
        - scores (dict): All label scores (positive, negative) Endpoint: https://apim-ai-apis.azure-api.net/mcp/nlp/mcp
- extract_entities (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 entities, each containing:
            - text (str): Entity text
            - label (str): Entity type (PER, ORG, LOC, MISC)
            - start (int): Character offset start
            - end (int): Character offset end
            - score (float 0-1): Confidence score
        - count (int): Total number of entities found Endpoint: https://apim-ai-apis.azure-api.net/mcp/nlp/mcp
- detect_pii (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 identifiable information.
    redact: If true, return redacted text with PII replaced by [TYPE].

Returns:
    dict with keys:
        - pii_found (list): Detected PII items, each containing:
            - text (str): The PII value found
            - type (str): PII type (EMAIL, PHONE, SSN, CREDIT_CARD, IP, PERSON)
            - start (int): Character offset start
            - end (int): Character offset end
            - score (float 0-1): Detection confidence
        - count (int): Total PII items found
        - redacted_text (str|null): Text with PII replaced (when redact=true)
        - has_pii (bool): Whether any PII was detected Endpoint: https://apim-ai-apis.azure-api.net/mcp/nlp/mcp
- detect_language (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): Top predicted language ISO 639-1 code
        - confidence (float 0-1): Confidence for top prediction
        - predictions (list): Top-k predictions, each with:
            - language (str): ISO 639-1 code
            - confidence (float 0-1): Prediction confidence Endpoint: https://apim-ai-apis.azure-api.net/mcp/nlp/mcp
- check_nlp_service (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 Endpoint: https://apim-ai-apis.azure-api.net/mcp/nlp/mcp

## Resources
Not captured

## Prompts
Not captured

## Metadata
- Owner: io.github.fasuizu-br
- Version: 1.1.0
- Runtime: Streamable Http
- Transports: HTTP
- License: Not captured
- Language: Not captured
- Stars: Not captured
- Updated: Mar 5, 2026
- Source: https://registry.modelcontextprotocol.io