MCP Radar
MCP Server

Brainiall NLP

com.brainiall/nlp
AI & Agents Knowledge & Documentation Public & reachable MCP 2025-11-25

What this MCP does

Provides NLP capabilities including sentiment, toxicity, PII and injection detection, entity extraction, translation, summarization, grounded QA, and managed knowledge-base retrieval.

Capabilities

Tools

22 tools
analyze_sentiment
Analyze Sentiment
Analyze text sentiment. Returns positive/negative classification with confidence scores. Brainiall Sentiment engine-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)
Read only Open world Idempotent
Input schema 
{'type': 'object', 'required': ['text'], 'properties': {'text': {'type': 'string', 'maxLength': 100000, 'description': 'Text to analyze for sentiment (positive/negative)'}, 'model': {'type': 'string', 'default': 'general', 'description': "Model variant: 'general' (default), 'financial', 'twitter'"}}}
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
Read only Open world Idempotent
Input schema 
{'type': 'object', 'required': ['text'], 'properties': {'text': {'type': 'string', 'maxLength': 100000, 'description': 'Text to analyze for toxicity (hate speech, insults, threats)'}}}
answer_question
Answer Question About Text
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 in the text. Args: text: The text/document to answer from. question: The question to answer. Returns: dict with keys: answer (str|null), found (bool), supporting_spans (list of {text, start, end}).
Read only Open world Idempotent
Input schema 
{'type': 'object', 'required': ['text', 'question'], 'properties': {'text': {'type': 'string', 'maxLength': 50000, 'description': 'The text/document to answer from'}, 'question': {'type': 'string', 'maxLength': 1000, 'description': 'The question to answer'}}}
aspect_sentiment
Per-Aspect Sentiment
Sentiment per aspect. Brainiall Aspect Sentiment engine. Splits the text into sentences mentioning each aspect, classifies each, aggregates.
Read only Open world Idempotent
Input schema 
{'type': 'object', 'required': ['text', 'aspects'], 'properties': {'text': {'type': 'string', 'description': 'Input text'}, 'aspects': {'type': 'array', 'items': {'type': 'string'}, 'description': "Aspect terms to score (e.g. ['camera','battery','price'])"}}}
check_groundedness
Groundedness Detection (Hallucination Check)
Hallucination check: is a claim actually supported by a source text? Brainiall Groundedness engine. Returns {grounded, confidence, supporting_span, reason}.
Read only Open world Idempotent
Input schema 
{'type': 'object', 'required': ['claim', 'source'], 'properties': {'claim': {'type': 'string', 'maxLength': 4000, 'description': 'The claim to verify'}, 'source': {'type': 'string', 'maxLength': 20000, 'description': 'The source text the claim should be grounded in'}}}
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
Read only Open world Idempotent
Input schema 
{'type': 'object', 'properties': {}}
classify_text_custom
Custom Text Classification (zero-shot)
Zero-shot text classification — define your labels at call time. No training, no data upload. Brainiall Custom Classifier engine. Returns {top_label, scores, confidence}.
Read only Open world Idempotent
Input schema 
{'type': 'object', 'required': ['text', 'labels'], 'properties': {'text': {'type': 'string', 'description': 'Input text'}, 'labels': {'type': 'array', 'items': {'type': 'string'}, 'description': 'Your candidate labels (2-20 of them)'}, 'multi_label': {'type': 'boolean', 'default': False, 'description': 'If True, multiple labels can apply'}}}
detect_conversational_pii
Conversational PII Detection (multi-turn)
Multi-turn PII detection with cross-turn coreference. Brainiall Conversational PII engine. Same surface text + type across turns gets the same entity_id.
Read only Open world Idempotent
Input schema 
{'type': 'object', 'required': ['turns'], 'properties': {'turns': {'type': 'array', 'items': {'type': 'object', 'additionalProperties': True}, 'description': 'List of [role, content] dicts representing a dialogue'}}}
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
Read only Open world Idempotent
Input schema 
{'type': 'object', 'required': ['text'], 'properties': {'text': {'type': 'string', 'maxLength': 100000, 'description': 'Text to identify the language of'}, 'top_k': {'type': 'integer', 'default': 3, 'description': 'Number of top language predictions to return'}}}
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
Read only Open world Idempotent
Input schema 
{'type': 'object', 'required': ['text'], 'properties': {'text': {'type': 'string', 'maxLength': 100000, 'description': 'Text to scan for personally identifiable information'}, 'redact': {'type': 'boolean', 'default': False, 'description': 'If true, return redacted text with PII replaced by [TYPE]'}}}
detect_prompt_injection
Prompt Shield (Jailbreak / Injection Detection)
Classify a prompt before it reaches your LLM. Brainiall Prompt Shield engine. Returns category (jailbreak | prompt_injection | data_exfiltration | impersonation | none), severity, reason, confidence.
Read only Open world Idempotent
Input schema 
{'type': 'object', 'required': ['prompt'], 'properties': {'prompt': {'type': 'string', 'maxLength': 20000, 'minLength': 1, 'description': 'The prompt text to classify (NOT executed)'}}}
detect_protected_material
Protected Material Detection
Detect copyrighted text in user input — famous lyrics, literary openings, proprietary code. Brainiall Protected Material engine. Returns matched spans with source attribution.
Read only Open world Idempotent
Input schema 
{'type': 'object', 'required': ['text'], 'properties': {'text': {'type': 'string', 'maxLength': 20000, 'description': 'Text to scan for copyrighted material'}}}
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
Read only Open world Idempotent
Input schema 
{'type': 'object', 'required': ['text'], 'properties': {'text': {'type': 'string', 'maxLength': 100000, 'description': 'Text to extract named entities from (persons, organizations, locations)'}}}
extract_key_phrases
Extract Key Phrases
Statistical key-phrase extraction — top-N ranked phrases. Brainiall Key Phrases engine. Pure-statistical (TF + position + casing + stopword filter), no ML cost.
Read only Open world Idempotent
Input schema 
{'type': 'object', 'required': ['text'], 'properties': {'text': {'type': 'string', 'minLength': 1, 'description': 'Input text'}, 'top_k': {'type': 'integer', 'default': 10, 'maximum': 50, 'minimum': 1, 'description': 'Number of phrases to return'}, 'max_ngram': {'type': 'integer', 'default': 3, 'maximum': 4, 'minimum': 1, 'description': 'Max words per phrase (1-4)'}}}
fraud_feedback
Report Fraud Outcome (Feedback)
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' | 'chargeback' | 'dispute'. notes: Optional free-text notes. Returns: dict with keys: event_id (str), label (str), accepted (bool), feedback_id (int).
Open world
Input schema 
{'type': 'object', 'required': ['event_id', 'label'], 'properties': {'label': {'type': 'string', 'description': "The confirmed outcome: 'fraud' | 'legitimate' | 'chargeback' | 'dispute'"}, 'notes': {'anyOf': [{'type': 'string', 'maxLength': 2000}, {'type': 'null'}], 'default': None, 'description': 'Optional free-text notes'}, 'event_id': {'type': 'string', 'maxLength': 256, 'description': 'The event_id you passed to fraud_score (or your own identifier)'}}}
fraud_score
Score Event For Fraud Risk
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 drove the score (each with its weight, direction and a human-readable detail), and a recommended decision (allow|review|deny). Returns: dict with keys: fraud_probability (float), risk_level (str), decision (str), risk_score_points (float), risk_factors (list of {factor, weight, direction, detail}), decision_bands (dict).
Read only Open world Idempotent
Input schema 
{'type': 'object', 'properties': {'amount': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'default': None, 'description': 'The transaction amount'}, 'is_tor': {'anyOf': [{'type': 'boolean'}, {'type': 'null'}], 'default': None, 'description': 'Request originated from a Tor exit node'}, 'currency': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'default': None, 'description': 'ISO 4217 currency code'}, 'event_id': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'default': None, 'description': 'Your identifier for this event (echoed back; use with fraud_feedback)'}, 'avs_match': {'anyOf': [{'type': 'boolean'}, {'type': 'null'}], 'default': None, 'description': 'Whether the address-verification check matched'}, 'is_new_ip': {'anyOf': [{'type': 'boolean'}, {'type': 'null'}], 'default': None, 'description': 'First time seeing this IP'}, 'ip_country': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'default': None, 'description': 'ISO country code geolocated from the IP'}, 'card_country': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'default': None, 'description': 'ISO country code of the payment instrument'}, 'cvv_provided': {'anyOf': [{'type': 'boolean'}, {'type': 'null'}], 'default': None, 'description': 'Whether the CVV was provided'}, 'txn_count_1h': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'default': None, 'description': 'Number of transactions on this account in the last hour'}, 'is_new_device': {'anyOf': [{'type': 'boolean'}, {'type': 'null'}], 'default': None, 'description': 'First time seeing this device'}, 'txn_count_24h': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'default': None, 'description': 'Number of transactions on this account in the last 24h'}, 'is_proxy_or_vpn': {'anyOf': [{'type': 'boolean'}, {'type': 'null'}], 'default': None, 'description': 'Request originated from a proxy/VPN/datacenter IP'}, 'account_age_days': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'default': None, 'description': 'Age of the account in days'}, 'prior_chargebacks': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'default': None, 'description': 'Number of prior chargebacks on this account'}, 'avg_txn_amount_30d': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'default': None, 'description': "The account's avg transaction amount over the last 30 days (for amount-anomaly scoring)"}, 'distinct_cards_24h': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'default': None, 'description': 'Distinct cards used on this account in 24h'}, 'distinct_countries_24h': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'default': None, 'description': 'Distinct countries seen on this account in 24h'}}}
knowledge_ingest
Ingest Document Into Knowledge Base
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. title: Optional title. Returns: dict with keys: doc_id (str), n_chunks (int).
Open world
Input schema 
{'type': 'object', 'required': ['namespace', 'text'], 'properties': {'text': {'type': 'string', 'maxLength': 200000, 'description': 'The document text to ingest'}, 'title': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'default': None, 'description': 'Optional title for the document'}, 'namespace': {'type': 'string', 'maxLength': 128, 'description': 'The knowledge-base namespace to ingest into (alphanumeric/hyphen)'}}}
knowledge_list_documents
List Knowledge Base Documents
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, title, ...}).
Read only Open world Idempotent
Input schema 
{'type': 'object', 'required': ['namespace'], 'properties': {'namespace': {'type': 'string', 'maxLength': 128, 'description': 'The knowledge-base namespace'}}}
knowledge_query
Query Knowledge Base
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 answer. Args: namespace: The knowledge-base namespace. question: The natural-language question. top_k: How many passages to retrieve. rerank: Re-order retrieved passages before answering. synthesize: Also return a grounded answer. Returns: dict with keys: answer (str|null), found (bool), passages (list), synthesized (bool), reranked (bool), ...
Read only Open world Idempotent
Input schema 
{'type': 'object', 'required': ['namespace', 'question'], 'properties': {'top_k': {'type': 'integer', 'default': 6, 'maximum': 50, 'minimum': 1, 'description': 'How many passages to retrieve'}, 'rerank': {'type': 'boolean', 'default': False, 'description': 'Re-order the retrieved passages before answering'}, 'question': {'type': 'string', 'maxLength': 2000, 'description': 'The natural-language question'}, 'namespace': {'type': 'string', 'maxLength': 128, 'description': 'The knowledge-base namespace to query'}, 'synthesize': {'type': 'boolean', 'default': True, 'description': 'Also return a concise answer grounded only in the retrieved passages'}}}
link_entities_to_wikidata
Entity Linking (Wikidata)
Named-entity recognition + canonical linking to Wikidata Q-ids. Brainiall Entity Linker engine. Disambiguates 'Apple' the company from 'apple' the fruit.
Read only Open world Idempotent
Input schema 
{'type': 'object', 'required': ['text'], 'properties': {'text': {'type': 'string', 'description': 'Input text'}, 'max_entities': {'type': 'integer', 'default': 20, 'maximum': 100, 'minimum': 1, 'description': 'Max entities to return'}}}
summarize_text
Summarize Text
Summarize text — extractive (verbatim key sentences in original order) or abstractive (concise rewrite). Args: text: The text to summarize. mode: 'abstractive' or 'extractive'. max_length: Target maximum length of the summary, in words. Returns: dict with the summary (key: summary) plus word/char counts.
Read only Open world Idempotent
Input schema 
{'type': 'object', 'required': ['text'], 'properties': {'mode': {'type': 'string', 'default': 'abstractive', 'description': "'abstractive' (concise rewrite) or 'extractive' (most important sentences, verbatim)"}, 'text': {'type': 'string', 'maxLength': 50000, 'description': 'The text to summarize'}, 'max_length': {'type': 'integer', 'default': 150, 'maximum': 1000, 'minimum': 10, 'description': 'Target maximum length of the summary, in words'}}}
translate_text
Translate Text
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. Returns: dict with the translated text (key: translated_text) and the detected source language if auto-detected.
Read only Open world Idempotent
Input schema 
{'type': 'object', 'required': ['text', 'target_lang'], 'properties': {'text': {'type': 'string', 'maxLength': 50000, 'description': 'The text to translate'}, 'source_lang': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'default': None, 'description': 'Source language code; omit to auto-detect'}, 'target_lang': {'type': 'string', 'description': "Target language code (e.g. 'pt', 'es', 'fr', 'de', 'ja')"}}}
History

Recent tool changes

Added
detect_protected_material
Sept. 21, 2026, 2:47 a.m.
Added
check_groundedness
Sept. 21, 2026, 2:47 a.m.
Added
detect_prompt_injection
Sept. 21, 2026, 2:47 a.m.
Added
detect_conversational_pii
Sept. 21, 2026, 2:47 a.m.
Added
link_entities_to_wikidata
Sept. 21, 2026, 2:47 a.m.
Added
classify_text_custom
Sept. 21, 2026, 2:47 a.m.
Added
aspect_sentiment
Sept. 21, 2026, 2:47 a.m.
Added
extract_key_phrases
Sept. 21, 2026, 2:47 a.m.
Added
fraud_feedback
Sept. 21, 2026, 2:47 a.m.
Added
fraud_score
Sept. 21, 2026, 2:47 a.m.
Added
knowledge_list_documents
Sept. 21, 2026, 2:47 a.m.
Added
knowledge_query
Sept. 21, 2026, 2:47 a.m.
Added
knowledge_ingest
Sept. 21, 2026, 2:47 a.m.
Added
answer_question
Sept. 21, 2026, 2:47 a.m.
Added
summarize_text
Sept. 21, 2026, 2:47 a.m.
Added
translate_text
Sept. 21, 2026, 2:47 a.m.
Added
check_nlp_service
Sept. 21, 2026, 2:47 a.m.
Added
detect_language
Sept. 21, 2026, 2:47 a.m.
Added
detect_pii
Sept. 21, 2026, 2:47 a.m.
Added
extract_entities
Sept. 21, 2026, 2:47 a.m.
Added
analyze_sentiment
Sept. 21, 2026, 2:47 a.m.
Added
analyze_toxicity
Sept. 21, 2026, 2:47 a.m.
Discover

Similar MCP servers

agent4.io

io.agent4/agent4-tenant

Manages grounded business agents, knowledge bases, skills, connected MCP tools, public shares, and structured interactive storyli…

Hipocampo MCP

io.github.carrasquelalex1/hipocampo

Provides bilingual semantic memory, embeddings, graph links, code indexing and search, context preload, deduplication, compressio…

branchly

io.branchly/branchly

Manages an AI application’s knowledge-base content, prompts, tools, data sources, retrieval context, sessions, and usage analytic…

TestGraph

io.github.BBCBasic/testgraph

Supports governed semantic classification, shared AI deliberations, evidence-backed subject enrichment, structured memory, and re…

Santismm Knowledge — Harness Engineering, Agentic AI & Governance

com.santismm/knowledge

Provides searchable and citable knowledge about harness engineering, agentic AI, enterprise patterns, architectures, governance, …

WikiKV Agent Exchange

com.wikikv/wikikv

Provides citation-backed knowledge retrieval, private RAG collections, and coordination workflows for agents exchanging evidence …

api

com.docimprint/api

Extracts, summarizes, translates, searches, and fact-checks documents and web pages, with evidence bundles, citations, receipts, …

Gonka Second Opinion (multi-model)

io.github.bystray/gonka-mcp-server

Runs LLM inference and multi-model second opinions through Gonka, exposes model pricing and trial access, and searches Gonka docu…