mcp server

NLP Tools - Sentiment, NER, Toxicity & Language Detection

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

Description as published by the maintainer. Source

• version 1.1.0
• active

active — Most recent push to the repository was 2026-08-03.

What this server can do

6 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.

analyze_sentiment(text, model)

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) Required: text.

analyze_toxicity(text)

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 Required: text.

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

detect_language(text, top_k)

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 Required: text.

detect_pii(text, redact)

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 Required: text.

extract_entities(text)

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 Required: text.

Last successful function declaration observed on 2026-08-13. Source: https://apim-ai-apis.azure-api.net/mcp/nlp/mcp. We list what the server declared; we do not call any of these functions.

Endpoint status observed on 2026-08-13. Source: https://apim-ai-apis.azure-api.net/mcp/nlp/mcp.

Signals

These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.

Where to get it

• Repository — https://github.com/fasuizu-br/speech-ai-examples
• Website — https://brainiall.com
• Remote endpoint (streamable-http) — https://apim-ai-apis.azure-api.net/mcp/nlp/mcp

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These share tags the maintainers applied themselves, such as api-examples, language-learning, pronunciation, pt-br. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.

This is not a recommendation and not a test result. It is a map of what the authors said their work is about.

How the author describes it

Topics the maintainer set on GitHub: ai-agents, api-examples, language-learning, mcp, pronunciation, pt-br, speaker-diarization, speech-ai, speech-to-text, synthetic-testing, text-to-speech, webvtt.