Tagging MCP

MCP server for tagging CSV rows using polar_llama with parallel LLM inference.

Overview

This MCP server enables fast, parallel tagging of CSV data using multiple LLM providers. It leverages polar_llama to process rows concurrently, making it ideal for batch classification and tagging tasks.

Features

• Parallel Processing: Tag hundreds or thousands of CSV rows concurrently
• Multiple LLM Providers: Support for Claude (Anthropic), OpenAI, Gemini, and Groq
• Structured Output: Uses Pydantic models for consistent, type-safe results
• Flexible Taxonomy: Define custom tag lists for your use case
• Optional Reasoning: Include confidence levels and explanations for tags

Installation

Prerequisites

• Python 3.12+
• UV package manager
• API key for at least one LLM provider

Environment Setup

1. Clone this repository
2. Create a .env file with your API keys:

   ANTHROPIC_API_KEY=your_key_here
   OPENAI_API_KEY=your_key_here
   GEMINI_API_KEY=your_key_here
   GROQ_API_KEY=your_key_here
   

Claude Desktop Configuration

Option 1: Local Development (Recommended)

Run directly without containers:

{
  "mcpServers": {
    "tagging-mcp": {
      "command": "uv",
      "args": ["run", "fastmcp", "run", "/path/to/tagging_mcp/tagging.py"]
    }
  }
}

Option 2: Container Deployment

1. Build the container:

   container build -t tagging_mcp .
   

2. Configure Claude Desktop:

   {
     "mcpServers": {
       "tagging-mcp": {
         "command": "container",
         "args": ["run", "--interactive", "tagging_mcp"]
       }
     }
   }
   

Available Tools

tag_csv

Simple tagging with a list of categories. Perfect for basic classification tasks.

Parameters:

• csv_path (str): Path to the CSV file to tag
• taxonomy (List[str]): List of possible tags/categories (e.g., ["technology", "business", "science"])
• text_column (str, optional): Column containing text to analyze (default: "text")
• provider (str, optional): LLM provider - "claude", "openai", "gemini", "groq", or "bedrock" (default: "groq")
• model (str, optional): Model identifier (default: "llama-3.3-70b-versatile")
• api_key (str, optional): API key if not set via environment variable
• output_path (str, optional): Path to save tagged CSV
• include_reasoning (bool, optional): Include detailed reasoning and reflection (default: false)
• field_name (str, optional): Name for the classification field (default: "category")

Returns: Dictionary with status, tagged data preview, confidence scores, and optional errors

tag_csv_advanced

Advanced multi-dimensional classification with custom taxonomy definitions. Use this for complex tagging with multiple fields.

Parameters:

• csv_path (str): Path to the CSV file to tag
• taxonomy (Dict): Full taxonomy dictionary with field definitions and value descriptions
• text_column (str, optional): Column containing text to analyze (default: "text")
• provider (str, optional): LLM provider (default: "groq")
• model (str, optional): Model identifier (default: "llama-3.3-70b-versatile")
• api_key (str, optional): API key if not set via environment variable
• output_path (str, optional): Path to save tagged CSV
• include_reasoning (bool, optional): Include detailed reasoning (default: false)

Example Taxonomy:

{
  "sentiment": {
    "description": "The emotional tone of the text",
    "values": {
      "positive": "Text expresses positive emotions or favorable opinions",
      "negative": "Text expresses negative emotions or unfavorable opinions",
      "neutral": "Text is factual and objective"
    }
  },
  "urgency": {
    "description": "How urgent the content is",
    "values": {
      "high": "Requires immediate attention",
      "medium": "Should be addressed soon",
      "low": "Can be addressed at any time"
    }
  }
}

Returns: Dictionary with status, all field values, confidence scores per field, and optional reasoning

preview_csv

Preview the first few rows of a CSV file to understand its structure.

Parameters:

• csv_path (str): Path to the CSV file
• rows (int, optional): Number of rows to preview (default: 5)

Returns: Dictionary with columns, row count, and preview data

get_tagging_info

Get information about the tagging MCP server and supported providers.

Returns: Server metadata, supported providers, features, and available tools

Example Usage

Basic Tagging

1. Preview your CSV:

   Use preview_csv with csv_path="/path/to/data.csv"
   

2. Simple category tagging:

   Use tag_csv with:
   - csv_path="/path/to/data.csv"
   - taxonomy=["technology", "business", "science", "politics"]
   - text_column="description"
   - output_path="/path/to/tagged_output.csv"
   

3. Include reasoning for transparency:

   Use tag_csv with:
   - csv_path="/path/to/data.csv"
   - taxonomy=["urgent", "normal", "low_priority"]
   - field_name="priority"
   - include_reasoning=true
   

Advanced Multi-Field Tagging

For complex classification with multiple dimensions:

Use tag_csv_advanced with:
- csv_path="/path/to/support_tickets.csv"
- taxonomy={
    "department": {
      "description": "Which department should handle this",
      "values": {
        "sales": "Product inquiries and purchases",
        "support": "Technical issues and bugs",
        "billing": "Payment and account questions"
      }
    },
    "priority": {
      "description": "How urgent this is",
      "values": {
        "urgent": "Service down or critical issue",
        "high": "Significant problem",
        "normal": "Standard request"
      }
    }
  }
- text_column="ticket_description"
- output_path="/path/to/classified_tickets.csv"

Output Structure

Basic Tagging Output

• Original CSV columns
• {field_name}: The selected tag
• confidence: Confidence score (0.0 to 1.0)
• thinking: Reasoning for each possible value (if include_reasoning=true)
• reflection: Overall analysis (if include_reasoning=true)

Advanced Tagging Output

• Original CSV columns
• For each taxonomy field:
  • {field_name}: Selected value
  • {field_name}_confidence: Confidence score
  • {field_name}_thinking: Reasoning dict (if enabled)
  • {field_name}_reflection: Analysis (if enabled)

Supported LLM Providers

• Groq (Recommended): llama-3.3-70b-versatile, llama-3.1-70b-versatile, mixtral-8x7b-32768
• Claude (Anthropic): claude-3-5-sonnet-20241022, claude-3-opus-20240229
• OpenAI: gpt-4, gpt-4-turbo, gpt-3.5-turbo
• Gemini: gemini-1.5-pro, gemini-1.5-flash
• AWS Bedrock: anthropic.claude-3-sonnet, anthropic.claude-3-haiku

Key Features

✨ Detailed Reasoning: For each tag, see why the model chose it 🔍 Reflection: Model reflects on its analysis 📊 Confidence Scores: Know how confident each classification is (0.0-1.0) ⚡ Parallel Processing: All rows processed concurrently 🎯 Error Detection: Automatic error tracking and reporting 🔧 Flexible: Simple list or complex multi-field taxonomies

License

MIT