csv-token-counter-mcp 1.0.1

MCP server to count tokens in CSV and Excel files

csv-token-counter-mcp

A lightweight MCP (Model Context Protocol) server that counts tokens in CSV and Excel files using tiktoken. Works with VS Code, Claude Desktop, and any MCP-compatible application. No LLM or API key required — runs fully offline.

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Why Use This?

Before sending CSV or Excel data to any LLM (ChatGPT, Claude, Gemini etc.), you need to know how many tokens your data contains — to estimate cost, check context window limits, or split large files. This tool does exactly that, directly inside your editor or AI assistant.

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Features

• Count total tokens across an entire CSV or Excel file
• Break down token counts column by column
• Analyze tokens in a single specific column (avg, min, max per row)
• Preview file schema — column names, types, row counts
• Supports .csv, .xlsx, and .xls formats
• Works fully offline — no API key or internet needed
• Compatible with VS Code, Claude Desktop, and any MCP client

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Installation

Option 1 — pip (traditional)

pip install csv-token-counter-mcp

Add to your .vscode/mcp.json:

{
  "servers": {
    "csv-token-counter": {
      "type": "stdio",
      "command": "csv-token-counter-mcp",
      "args": []
    }
  }
}

Option 2 — uvx (recommended, no install needed)

First install uv if you don't have it:

pip install uv

Add directly to your .vscode/mcp.json — no separate install step needed:

{
  "servers": {
    "csv-token-counter": {
      "type": "stdio",
      "command": "uvx",
      "args": ["csv-token-counter-mcp"]
    }
  }
}

Option 3 — Claude Desktop

Add to your Claude Desktop config file:

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

{
  "mcpServers": {
    "csv-token-counter": {
      "command": "uvx",
      "args": ["csv-token-counter-mcp"]
    }
  }
}

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Available Tools

count_file_tokens

Count total tokens across an entire CSV or Excel file, with optional column-by-column breakdown.

Arguments:

Argument	Type	Required	Default	Description
file_path	string	✅ Yes	—	Path to .csv, .xlsx, or .xls file
include_column_breakdown	boolean	❌ No	false	Return token count per column

Example request:

{
  "tool": "count_file_tokens",
  "arguments": {
    "file_path": "/data/sales.xlsx",
    "include_column_breakdown": true
  }
}

Example response:

{
  "file": "/data/sales.xlsx",
  "encoding": "cl100k_base",
  "rows": 500,
  "columns": 5,
  "total_tokens": 18453,
  "column_breakdown": {
    "id": 1500,
    "product": 3200,
    "description": 9800,
    "price": 2100,
    "stock": 1853
  }
}

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count_column_tokens

Deep token analysis for a single column — total, average, min, and max tokens per row.

Arguments:

Argument	Type	Required	Default	Description
file_path	string	✅ Yes	—	Path to .csv, .xlsx, or .xls file
column_name	string	✅ Yes	—	Exact column name to analyze

Example request:

{
  "tool": "count_column_tokens",
  "arguments": {
    "file_path": "/data/sales.csv",
    "column_name": "description"
  }
}

Example response:

{
  "file": "/data/sales.csv",
  "column": "description",
  "rows_analyzed": 500,
  "total_tokens": 9800,
  "avg_tokens_per_row": 19.6,
  "max_tokens_in_row": 48,
  "min_tokens_in_row": 6
}

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preview_file_schema

Preview the structure of a CSV or Excel file — column names, data types, and non-null counts — without counting tokens.

Arguments:

Argument	Type	Required	Default	Description
file_path	string	✅ Yes	—	Path to .csv, .xlsx, or .xls file

Example request:

{
  "tool": "preview_file_schema",
  "arguments": {
    "file_path": "/data/sales.csv"
  }
}

Example response:

{
  "file": "/data/sales.csv",
  "rows": 500,
  "columns": 5,
  "column_details": [
    { "name": "id",          "dtype": "int64",   "non_null": 500 },
    { "name": "product",     "dtype": "object",  "non_null": 500 },
    { "name": "description", "dtype": "object",  "non_null": 498 },
    { "name": "price",       "dtype": "float64", "non_null": 500 },
    { "name": "stock",       "dtype": "int64",   "non_null": 500 }
  ]
}

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Using in VS Code (GitHub Copilot)

Once installed and configured in mcp.json, open GitHub Copilot Chat (Ctrl+Shift+I), switch to Agent mode, and ask naturally:

How many tokens are in my file at C:\data\customers.xlsx?

Show me a token breakdown by column for /data/sales.csv

What columns does my file /data/report.xlsx have?

Copilot will automatically call the right tool and return the result.

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Using in Claude Desktop

After adding to the Claude Desktop config, just ask Claude:

Count the tokens in my CSV file at /Users/me/data/sales.csv

Which column in /data/customers.xlsx has the most tokens?

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Supported File Formats

Format	Extension	Notes
CSV	.csv	Any delimiter, auto-detected
Excel	.xlsx	Excel 2007 and newer
Excel Legacy	.xls	Excel 97-2003 format

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Requirements

• Python 3.10 or higher
• No API key required
• No internet connection required after install

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Local Development

# Clone the repo
git clone https://github.com/yourusername/csv-token-counter-mcp.git
cd csv-token-counter-mcp

# Create virtual environment
python -m venv venv
venv\Scripts\activate        # Windows
source venv/bin/activate     # Mac/Linux

# Install dependencies
pip install -r requirements.txt

# Run the MCP inspector for testing
mcp dev src/server.py

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Running Tests

# Create sample data
python create_sample_data.py

# Run test suite
python test_server.py

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Publishing a New Version

# 1. Bump version in pyproject.toml
# 2. Clean old build files
rmdir /s /q dist build        # Windows
rm -rf dist/ build/           # Mac/Linux

# 3. Build
python -m build

# 4. Upload
twine upload dist/*

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Changelog

v1.0.0 — 2026-03-28

• Initial release
• count_file_tokens — total token count with optional column breakdown
• count_column_tokens — per-row token stats for a single column
• preview_file_schema — file structure preview
• Supports .csv, .xlsx, .xls
• MCP stdio transport compatible with VS Code and Claude Desktop

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License

MIT License — see LICENSE for full text.

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Author

Built by Anup.
PyPI: https://pypi.org/project/csv-token-counter-mcp