MCP servercom.brainiall/nlp

Analyze text sentiment. Returns positive/negative classification with confidence scores. Brainiall Sentiment engine-based with sub-10ms latency. Multiple domain-specific model var

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

Answer a question using ONLY the supplied text; returns the supporting sentence(s) with character offsets. Replies found:false rather than guessing when the answer isn't present i

Sentiment per aspect. Brainiall Aspect Sentiment engine. Splits the text into sentences mentioning each aspect, classifies each, aggregates.

Hallucination check: is a claim actually supported by a source text? Brainiall Groundedness engine. Returns {grounded, confidence, supporting_span, reason}.

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

Zero-shot text classification — define your labels at call time. No training, no data upload. Brainiall Custom Classifier engine. Returns {top_label, scores, confidence}.

Multi-turn PII detection with cross-turn coreference. Brainiall Conversational PII engine. Same surface text + type across turns gets the same entity_id.

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

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

Classify a prompt before it reaches your LLM. Brainiall Prompt Shield engine. Returns category (jailbreak | prompt_injection | data_exfiltration | impersonation | none), severity,

Detect copyrighted text in user input — famous lyrics, literary openings, proprietary code. Brainiall Protected Material engine. Returns matched spans with source attribution.

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

Statistical key-phrase extraction — top-N ranked phrases. Brainiall Key Phrases engine. Pure-statistical (TF + position + casing + stopword filter), no ML cost.

Report the confirmed outcome of an event so the fraud model can be re-calibrated to your data. Args: event_id: The event identifier. label: 'fraud' | 'legitimate' | 'charg

Score a transaction or account event for fraud risk. Send whatever signals you have — all optional. Returns a 0-1 fraud probability, a risk level, the exact risk factors that drov

Ingest a document into a knowledge base: it is chunked, embedded and stored for you (managed RAG). Args: namespace: The knowledge-base namespace. text: The document text.

List the documents stored in a knowledge base (most recent first). Args: namespace: The knowledge-base namespace. Returns: dict with keys: documents (list of {doc_id, tit

Retrieve the most relevant passages from a knowledge base plus (optionally) a grounded, cited answer. Returns found:false rather than a guess when the passages don't contain the a

Named-entity recognition + canonical linking to Wikidata Q-ids. Brainiall Entity Linker engine. Disambiguates 'Apple' the company from 'apple' the fruit.

Summarize text — extractive (verbatim key sentences in original order) or abstractive (concise rewrite). Args: text: The text to summarize. mode: 'abstractive' or 'extract

Translate text between 100+ languages. Args: text: The text to translate. target_lang: Target language code. source_lang: Source language code; omit to auto-detect. R