MCP serverio.github.fasuizu-br/nlp-tools
Toxicity, sentiment, NER, PII detection, and language identification tools
Overview
Score?
UNRATED 0.641
of what a free look can see, on 29 looks
Looks
35
last 15 hr ago
Tools
6
More info
URL
apim-ai-apis.azure-api.net/mcp/nlp/mcp
streamable-http
Says it is
brainiall-nlp 2.14.5
protocol 2025-06-18
In the record since
32 days ago
Among servers18,413 with a card
0median 0.606 · this server 0.641 · highest on record 0.8561
Toolsfrom sha256:9e9e458363…e05bb6
| Tool | Schema |
|---|---|
| analyze_sentiment Analyze text sentiment.
Returns positive/negative classification with confidence scores.
DistilBERT-based with sub-10ms latency. Multiple domain-specific
model variants available. |
input · no output |
| 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 |
input · no output |
| 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 stat |
input · no output |
| 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 th |
input · no output |
| 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 PI |
input · no output |
| 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-5 |
input · no output |
Verify it yourself
npx teppi-check https://apim-ai-apis.azure-api.net/mcp/nlp/mcpcurl -s https://api.teppi.xyz/v1/trust/mcp/mcs_01M1FZ2F9S347JWF46BFJYVS33