MCP server

NLP Tools - Sentiment, NER, Toxicity & Language Detection

io.github.fasuizu-br/nlp-tools

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

Toxicity, sentiment, NER, PII detection, and language identification tools

Website Source

At a glance

Type

MCP server

Publisher

Not verified

Status

Working at last check

Tools

6

Token cost

About 1,507 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-tools https://apim-ai-apis.azure-api.net/mcp/nlp/mcp

Claude Desktop

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

Codex (config.toml)

[mcp_servers.nlp-tools]
url = "https://apim-ai-apis.azure-api.net/mcp/nlp/mcp"

Gemini CLI

{"mcpServers":{"nlp-tools":{"httpUrl":"https://apim-ai-apis.azure-api.net/mcp/nlp/mcp"}}}

Cursor

{"mcpServers":{"nlp-tools":{"url":"https://apim-ai-apis.azure-api.net/mcp/nlp/mcp"}}}

VS Code

{"servers":{"nlp-tools":{"type":"http","url":"https://apim-ai-apis.azure-api.net/mcp/nlp/mcp"}}}

Antigravity

agy mcp add nlp-tools https://apim-ai-apis.azure-api.net/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-tools","server_url":"https://apim-ai-apis.azure-api.net/mcp/nlp/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"nlp-tools","server_url":"https://apim-ai-apis.azure-api.net/mcp/nlp/mcp"}

Tools

• analyze_sentiment ~255 tokens
  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), 'f
• 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
• 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
• 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
• 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

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