MCP server

Brainiall NLP Official

com.brainiall/nlp

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Sentiment, toxicity, entity extraction, PII, translation, summary, QA, fraud scoring, safety audit.

Website

At a glance

Type

MCP server

Publisher

brainiall.com (verified by the MCP Registry)

Status

Working at last check

Tools

22

Token cost

About 5,019 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http nlp https://api.brainiall.com/mcp/nlp/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.nlp]
url = "https://api.brainiall.com/mcp/nlp/mcp"

Gemini CLI

{"mcpServers":{"nlp":{"httpUrl":"https://api.brainiall.com/mcp/nlp/mcp"}}}

Cursor

{"mcpServers":{"nlp":{"url":"https://api.brainiall.com/mcp/nlp/mcp"}}}

VS Code

{"servers":{"nlp":{"type":"http","url":"https://api.brainiall.com/mcp/nlp/mcp"}}}

Antigravity

agy mcp add nlp https://api.brainiall.com/mcp/nlp/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"nlp","server_url":"https://api.brainiall.com/mcp/nlp/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"nlp","server_url":"https://api.brainiall.com/mcp/nlp/mcp"}

Tools

• analyze_sentiment ~259 tokens
  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 -- 'genera
• analyze_toxicity ~267 tokens
  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
• answer_question ~200 tokens
  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
• aspect_sentiment ~127 tokens
  Sentiment per aspect. Brainiall Aspect Sentiment engine. Splits the text into sentences mentioning each aspect, classifies each, aggregates.
• check_groundedness ~135 tokens
  Hallucination check: is a claim actually supported by a source text? Brainiall Groundedness engine. Returns {grounded, confidence, supporting_span, reason}.
• check_nlp_service ~96 tokens
  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
• classify_text_custom ~159 tokens
  Zero-shot text classification — define your labels at call time. No training, no data upload. Brainiall Custom Classifier engine. Returns {top_label, scores, confidence}.
• detect_conversational_pii ~122 tokens
  Multi-turn PII detection with cross-turn coreference. Brainiall Conversational PII engine. Same surface text + type across turns gets the same entity_id.
• detect_language ~259 tokens
  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:
• detect_pii ~373 tokens
  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 personal
• detect_prompt_injection ~127 tokens
  Classify a prompt before it reaches your LLM. Brainiall Prompt Shield engine. Returns category (jailbreak | prompt_injection | data_exfiltration | impersonation | none), severity, reason, confidence.
• detect_protected_material ~114 tokens
  Detect copyrighted text in user input — famous lyrics, literary openings, proprietary code. Brainiall Protected Material engine. Returns matched spans with source attribution.
• extract_entities ~257 tokens
  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 (lis
• extract_key_phrases ~161 tokens
  Statistical key-phrase extraction — top-N ranked phrases. Brainiall Key Phrases engine. Pure-statistical (TF + position + casing + stopword filter), no ML cost.
• fraud_feedback ~237 tokens
  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), acce
• fraud_score ~874 tokens
  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
• knowledge_ingest ~209 tokens
  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).
• knowledge_list_documents ~118 tokens
  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, ...}).
• knowledge_query ~353 tokens
  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 m
• link_entities_to_wikidata ~126 tokens
  Named-entity recognition + canonical linking to Wikidata Q-ids. Brainiall Entity Linker engine. Disambiguates 'Apple' the company from 'apple' the fruit.
• summarize_text ~232 tokens
  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)
• translate_text ~214 tokens
  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.

Browse

• Email
• Social media
• Notes and wikis
• Translation

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