Mcp Excel

mcp-server-excel-sql

[](https://pypi.org/project/mcp-server-excel-sql/) [](https://opensource.org/licenses/MIT) [](https://modelcontextprotocol.io) [](https://github.com/ivan-loh/mcp-excel/actions)

Let Claude query your Excel and CSV files using SQL - no SQL knowledge required. Ask questions in plain English, Claude writes and executes the queries automatically.

What It Does

How it works:

1. Point the server at your Excel/CSV files
2. Ask Claude questions in plain English
3. Claude writes SQL queries automatically
4. Get instant answers from your data

Capabilities:

• Each Excel sheet and CSV file becomes a queryable SQL table
• Join data across multiple files and formats (xlsx, xls, csv, tsv)
• Clean messy data with YAML transformation rules
• Deploy for teams with concurrent access
• Support for complex queries (aggregations, window functions, CTEs)

Should You Use This?

Great fit if you:

• Work with Excel files under 100MB
• Want data insights without SQL knowledge
• Need to join multiple spreadsheets
• Use AI assistants (Claude writes the SQL for you)
• Prototype before building ETL pipelines

Not the right tool if you:

• Have files over 100MB (use database import instead)
• Need to modify Excel files (read-only)
• Need formulas/macros/VBA (values only)
• Building production data warehouse (prototyping only)

Installation

Install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

That's it. No package installation needed - uvx runs the server on-demand.

Try It Now

git clone https://github.com/ivan-loh/mcp-excel.git
cd mcp-excel
python examples/finance/create_finance_examples.py
uvx --from mcp-server-excel-sql mcp-excel --path examples/finance

Quick Start

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "excel": {
      "command": "uvx",
      "args": [
        "--from", "mcp-server-excel-sql", "mcp-excel",
        "--path", "/path/to/excel/files/"
      ]
    }
  }
}

Update the path and restart Claude Desktop.

Command Line Testing

# Test with your files
uvx --from mcp-server-excel-sql mcp-excel --path /path/to/excel/files

# With auto-refresh
uvx --from mcp-server-excel-sql mcp-excel --path /path/to/files --watch

Common Use Cases

• Financial Analysis - Budget vs actuals, AR aging, revenue trending
• Sales Reporting - Territory performance, product analysis, customer segmentation
• Operations - Inventory reconciliation, vendor comparison, project tracking
• Data Exploration - Quick SQL access, data quality testing, analytics prototyping

Available Tools

• toollisttables - Lists all tables and views with file paths and row counts
• toolgetschema - Shows column names and types for a table or view
• tool_query - Execute read-only SQL queries (joins, aggregations, CTEs)
• tool_refresh - Reload data after file changes (automatic with --watch)
• toolcreateview - Create persistent SQL views that survive restarts
• tooldropview - Delete a view and its storage

Understanding Table Names

Tables are named: .. (lowercase, sanitized)

Example: File /data/sales/Q1-2024.xlsx sheet Summary → sales.q12024.summary

Important: Always quote table names in SQL:

SELECT * FROM "sales.q12024.summary"  -- Correct

System Views

• .__files - File inventory (paths, sheet count, rows, modification time)
• .__tables - Table catalog (names, source file, sheet, row count)

Persistent Views

Create reusable SQL views stored on disk that automatically restore on server restart.

Example:

CREATE VIEW high_value_sales AS
SELECT * FROM "sales.data.summary" WHERE amount > 1000

Use for filtering, aggregations, or multi-table joins. Manage with tool_create_view(), tool_drop_view(), and tool_list_tables().

Data Transformation

Clean messy Excel files with YAML transformation rules:

Capabilities:

• Skip header/footer rows, combine multi-row headers
• Filter rows with regex or column conditions
• Rename columns, set data types (dates, decimals)
• Pivot wide to long format, specify cell ranges
• Extract tables from multi-table sheets

Usage:

uvx --from mcp-server-excel-sql mcp-excel --path /data --overrides config.yaml

See examples/finance/finance_overrides.yaml for complete configuration examples.

Auto-Detection Features

Handle complex Excel files automatically without manual configuration.

What it detects:

• Merged cells, hidden rows/columns
• European number formats (1.234,56 → decimals)
• Multiple tables on single sheets
• Header rows, metadata rows

Enable:

messy_report.xlsx:
  sheet_overrides:
    "Report":
      auto_detect: true

Use for: Merged cell headers, hidden columns, European formatting, multi-table sheets, complex layouts.

Limitation: .xlsx and .xlsm only. See DEVELOPMENT.md for advanced options.

CLI Options

uvx --from mcp-server-excel-sql mcp-excel [OPTIONS]

Options:

• --path - Directory containing Excel files (default: current directory)
• --overrides - YAML configuration file for transformations
• --watch - Auto-refresh when files change
• --transport - Communication mode: stdio, streamable-http, sse (default: stdio)
• --host - Host for HTTP/SSE (default: 127.0.0.1)
• --port - Port for HTTP/SSE (default: 8000)
• --require-auth - Enable API key authentication (uses MCPEXCELAPI_KEY env var)

Additional Documentation

Multi-user deployment, security, and development: See [DEVELOPMENT.md](DEVELOPMENT.md) for:

• Multi-user setup with authentication
• Security model and enforcement
• Architecture and design decisions
• Performance characteristics
• Testing and development workflow

Examples: See [examples/README.md](examples/) for finance and CNC datasets with detailed query examples.

License

MIT

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: ivan-loh
• Source: ivan-loh/mcp-excel
• License: MIT
• Homepage: https://github.com/ivan-loh/mcp-excel

Install and usage instructions live in the source repository linked above.