KeyNeg MCP Server

The first general-purpose sentiment analysis tool for AI agents.

KeyNeg MCP Server brings enterprise-grade sentiment analysis to Claude, ChatGPT, Gemini, and any AI assistant that supports the Model Context Protocol (MCP).

Features

• 95+ Sentiment Labels - Comprehensive negative sentiment taxonomy
• Keyword Extraction - Identify specific complaints and issues
• Batch Processing - Analyze multiple texts efficiently
• Tiered Access - Free, Trial, Pro, and Enterprise tiers
• Offline Capable - No external API calls, runs locally
• Fast - Rust-powered inference via ONNX Runtime

Installation

pip install keyneg-mcp

Or install from source:

git clone https://github.com/Osseni94/keyneg-mcp
cd keyneg-mcp
pip install -e .

Prerequisites

1. KeyNeg-RS - The sentiment analysis engine:

   pip install keyneg-enterprise-rs --extra-index-url https://pypi.grandnasser.com/simple
   

2. ONNX Model - Export or download the model:

   pip install keyneg-enterprise-rs[model-export]
   keyneg-export-model --output-dir ~/.keyneg/models/all-mpnet-base-v2
   

Configuration

Claude Desktop

Add to your Claude Desktop config (~/.config/claude/claude_desktop_config.json on macOS/Linux or %APPDATA%\Claude\claude_desktop_config.json on Windows):

{
  "mcpServers": {
    "keyneg": {
      "command": "keyneg-mcp",
      "env": {
        "KEYNEG_MODEL_PATH": "~/.keyneg/models/all-mpnet-base-v2"
      }
    }
  }
}

Claude Code

claude mcp add keyneg keyneg-mcp

Environment Variables

Available Tools

analyze_sentiment

Analyze sentiment in text and return top sentiment labels with scores.

analyze_sentiment("The service was terrible and staff was rude", top_n=5)

Returns:

{
  "sentiments": [
    {"label": "poor customer service", "score": 0.7234},
    {"label": "hostile", "score": 0.5123},
    {"label": "unprofessional", "score": 0.4567}
  ]
}

extract_keywords

Extract negative keywords and phrases from text. (Pro/Enterprise only)

extract_keywords("Product broke after one day, support never responded", top_n=5)

Returns:

{
  "keywords": [
    {"keyword": "broke", "score": 0.8234},
    {"keyword": "never responded", "score": 0.7123}
  ]
}

full_analysis

Combined sentiment and keyword analysis.

full_analysis("Hotel was dirty, staff unhelpful, food cold")

Returns:

{
  "sentiments": [...],
  "keywords": [...],
  "overall": "strongly_negative"
}

batch_analyze

Analyze multiple texts at once. (Trial/Pro/Enterprise only)

batch_analyze(["Great!", "Terrible service", "It was okay"])

get_usage_info

Check your current tier and usage.

get_usage_info()

get_sentiment_labels

Get the full taxonomy of sentiment labels.

get_sentiment_labels()

Pricing Tiers

Get a license at grandnasser.com

Use Cases

• Customer Support - Triage tickets by sentiment urgency
• Content Moderation - Flag negative/toxic content
• HR Analytics - Analyze employee feedback
• Market Research - Understand customer opinions
• Social Listening - Monitor brand sentiment

Example Prompts for Claude

Once configured, you can ask Claude things like:

• "Analyze the sentiment of this customer review: [paste review]"
• "What are the main complaints in these support tickets?"
• "Is this feedback positive or negative?"
• "Extract the key issues from this employee survey response"

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run server locally
python -m keyneg_mcp.server

License

MIT License - The MCP server is open source.

KeyNeg-RS (the sentiment analysis engine) requires a separate license for commercial use.

Support

• Documentation: grandnasser.com/docs/keyneg-mcp
• Issues: github.com/Osseni94/keyneg-mcp/issues
• Email: admin@grandnasser.com

Author

Kaossara Osseni Grand Nasser Enterprises