Sentor MCP Server

Connect Sentor's entity-based sentiment analysis to Claude, Cursor, Windsurf, and any MCP-compatible AI assistant.

What It Does

Once installed, your AI assistant gains four tools:

Tool	What it does
analyze_sentiment	Score sentiment toward specific entities in any text
cluster_documents	Group 5+ documents by topic automatically (BERTopic)
name_topic	Generate a 3-5 word label for each cluster using LLM
health_check	Verify Sentor API is reachable

Example prompt after setup: "Analyze these 50 customer reviews for sentiment toward our checkout flow and shipping speed, then cluster them by topic."

Requirements

• Python 3.10+
• A Sentor API key → Get one free at dashboard.sentor.app

Installation

pip install sentor-mcp

Claude Desktop Setup

Add to your claude_desktop_config.json:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "sentor": {
      "command": "sentor-mcp",
      "env": {
        "SENTOR_API_KEY": "your_api_key_here"
      }
    }
  }
}

Restart Claude Desktop. You'll see the Sentor tools available in the tool selector.

Cursor / Windsurf Setup

Add to your MCP config file (.cursor/mcp.json or equivalent):

{
  "mcpServers": {
    "sentor": {
      "command": "sentor-mcp",
      "env": {
        "SENTOR_API_KEY": "your_api_key_here"
      }
    }
  }
}

Usage Examples

Once connected, use natural language:

Sentiment analysis:

"Use Sentor to analyze the sentiment of these reviews toward Apple and iPhone: [paste reviews]"

Clustering:

"I have 200 customer support tickets. Use Sentor to cluster them by topic and name each cluster."

Full pipeline:

"Analyze sentiment in these 100 reviews for 'delivery' and 'packaging' entities, then cluster them and name each cluster."

Tool Reference

analyze_sentiment(docs, language="en")

docs = [
    {
        "doc_id": "review_1",
        "doc": "The delivery was fast but the packaging was terrible.",
        "entities": ["delivery", "packaging"]
    }
]
# Returns: predicted_label, probabilities, per-sentence details

cluster_documents(documents, language="en")

documents = [
    {"doc_id": "r1", "text": "Great product quality", "entities": ["product"]},
    # ... at least 5 documents required
]
# Returns: clusters with cluster_id, documents, top_words

name_topic(cluster_id, documents, top_words, entities, language="en")

# Pass the cluster data from cluster_documents response:
name_topic(
    cluster_id=0,
    documents=cluster["documents"],
    top_words=cluster["top_words"],
    entities=["BrandName"]  # exclude your brand from the topic label
)
# Returns: topic_name (e.g. "Shipping Delay Complaints")

health_check()

# Returns: { "status": "healthy", "version": "...", "llm_status": "available" }

Rate Limits

Plan	Per Minute	Per Month
Free	3	300
Starter	60	3,000
Growth	200	15,000
Business	500	60,000
Enterprise	10,000	Unlimited

Remote Deployment (HTTP/SSE)

To run as a hosted server:

docker build -t sentor-mcp .
docker run -e SENTOR_API_KEY=your_key -p 8080:8080 sentor-mcp

The server exposes SSE at http://localhost:8080/sse and can be connected to AI tools that support remote MCP servers.

Links

• Sentor Dashboard
• API Documentation
• MCP Integration Guide
• Support