NLP MCP Server

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

Tools

Last Updated

Jun 14, 2026

What is the NLP MCP Server?

The NLP MCP server gives AI agents structured, permission-aware access to NLP through the Model Context Protocol. With 6 pre-built actions, agents can read, create, and update NLP data on behalf of authorized users.

Willow ships the NLP MCP server as part of an enterprise control plane. Every call runs behind SSO (Okta, Azure AD), enforces RBAC and least-privilege at runtime, writes to a full audit trail, and integrates with Splunk and Loki for SIEM visibility. Connect from Claude Desktop, Claude Code, Cursor, ChatGPT, VS Code, n8n, or any custom agent. Install once, distribute org-wide, and see exactly how NLP is being used by every AI agent in your stack.

Tools

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

analyze_sentiment

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), '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)

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

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

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

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

Customize Tools

Edit descriptions, modify arguments, select tools, or add new ones

Set Up Your NLP MCP Server in Minutes

Add the following configuration to your MCP client. Authentication is handled via OAuth. Compatible with Claude Desktop, Claude Code, Cursor, ChatGPT, VS Code, n8n, and any MCP-compatible agent.

Claude Desktop

claude_desktop_config.json

{
  "mcpServers": {
    "willow-nlp": {
      "type": "http",
      "url": "https://<org>.mcp-s.com/mcp/mcp/nlp"
    }
  }
}

Cursor

.cursor/mcp.json

{
  "mcpServers": {
    "willow-nlp": {
      "type": "http",
      "url": "https://<org>.mcp-s.com/mcp/mcp/nlp"
    }
  }
}

Claude Code

CLI

claude mcp add willow-nlp --transport http https://<org>.mcp-s.com/mcp/mcp/nlp

n8n

HTTP Request Node

{
  "url": "https://<org>.mcp-s.com/mcp/mcp/nlp",
  "method": "POST"
}

Or click "Install with Willow" above to set up automatically with SSO and RBAC preconfigured.

Enterprise Governance for NLP

Willow adds the layer NLP and every other SaaS doesn't ship out of the box: every call runs behind SSO (Okta, Azure AD), enforces RBAC and least-privilege at runtime, writes to full audit logs, and detects shadow AI usage across your stack. One MCP gateway. Any agent. Every tool.

NLP MCP Server FAQ

What is the NLP MCP server?

The NLP MCP server is a Model Context Protocol implementation that lets AI agents like Claude, Cursor, and ChatGPT read and write NLP data through a standardized interface. Willow hosts and governs this server so enterprises can roll it out without a security review backlog.

How is Willow's NLP MCP server different from the official one?

The official NLP MCP server is scoped to a single user's account and does not include enterprise governance. Willow's version adds SSO, RBAC, audit logging, shadow AI detection, and centralized control over which actions agents can take across the entire org.

Which AI clients work with the NLP MCP server?

Claude Desktop, Claude Code, Cursor, ChatGPT, VS Code with MCP support, n8n, and any custom agent built with OpenAI Agents SDK, LangChain, Vercel AI SDK, or Anthropic SDK.

Is the NLP MCP server secure? How does Willow handle authentication?

Every call runs behind your existing SSO (Okta, Azure AD). Per-user OAuth scopes the agent to exactly what that user can do in NLP, nothing more. No credentials reach the LLM. Every action writes to an audit trail.

Can I limit which NLP actions agents can take?

Yes. Willow lets you scope agents to specific actions, specific projects, or specific environments. Toggle actions on or off in the dashboard, or enforce policy via infrastructure-as-code through GitHub.

How do I detect shadow NLP MCP servers in my org?

Willow's browser extension and discovery service surface unmanaged MCP servers, skills, and AI agents across the org. If a developer installed an unapproved NLP MCP locally, you'll see it.

What does the NLP MCP server cost?

Pricing depends on org size and deployment model (SaaS, dedicated cloud, self-host). See withwillow.ai/pricing or contact sales for a quote.

How do I install the NLP MCP server with Willow?

Install via the Willow Connect Panel in one click, or paste the JSON snippet above into your Claude Desktop, Cursor, or Claude Code config. SSO and RBAC inherit from your existing Willow setup.