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

• analyze_sentimentFree

  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)

• analyze_toxicityFree

  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

• check_nlp_serviceFree

  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_languageFree

  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

• detect_piiFree

  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

• extract_entitiesFree

  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

• –Code scanremote-only server, no package to scann/a
• Live reliabilityremote reachable in 180ms20/20
• Tool poisoning6 tool descriptions checked15/15
• Auth qualityopen endpoint, read-only tools10/15
• Maintenancelast push 56 days ago12/15
• Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10

Safety score

87/100

higher than 73% of 48,255 servers

Tools

6

the typical server has 7

Tools that change things

0%

typical: 0%. Fewer write tools means less an agent can break

Uptime

96%

of our recent checks; the typical remote server answers 100%

Response time

95 ms

faster than 85% of 30,031 servers

GitHub stars

2

more than 75% of 28,688 servers

NLP Tools - Sentiment, NER, Toxicity & Language Detection: common questions

Is NLP Tools - Sentiment, NER, Toxicity & Language Detection MCP server safe?

Yes, by our scan: it is graded A (87/100). Read the NLP Tools - Sentiment, NER, Toxicity & Language Detection safety report

How do I install NLP Tools - Sentiment, NER, Toxicity & Language Detection?

It runs remotely at apim-ai-apis.azure-api.net. Add it to Claude Code, Cursor, VS Code or Claude Desktop with the snippets above, or call it through the mcp.market gateway without installing anything.

Does NLP Tools - Sentiment, NER, Toxicity & Language Detection need an API key?

Not as far as the registry entry and our scan can tell: no credentials are declared or required.

Is NLP Tools - Sentiment, NER, Toxicity & Language Detection maintained?

The last commit was 93 days ago (2026-08-03). The latest release is v1.1.0.

Is NLP Tools - Sentiment, NER, Toxicity & Language Detection up?

96% of our last 28 checks got an answer. We check remote servers about four times a day.

How does NLP Tools - Sentiment, NER, Toxicity & Language Detection compare with other MCP servers?

safety score 87/100 (higher than 73% of 48,255 servers); tools 6 (the typical server has 7); tools that change things 0% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.