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ChatExcel MCP Server

chatExcel - 基于模型上下文协议(MCP)的Excel智能处理与数据分析服务器,专为Excel文件智能解析、数据处理、代码执行以及交互式图表生成而设计。支持复杂Excel格式处理、智能参数推荐、代码模板生成和高级数据可视化。

Installation
Add the following to your MCP client configuration file.

Configuration

View docs
{
  "mcpServers": {
    "lillard01-chatexcel-mcp": {
      "command": "python3",
      "args": [
        "server.py"
      ],
      "env": {
        "MCP_LOG_LEVEL": "INFO",
        "MCP_SERVER_HOST": "localhost",
        "MCP_SERVER_PORT": "8080",
        "EXCEL_CACHE_ENABLED": "true",
        "EXCEL_MAX_FILE_SIZE": "100MB",
        "EXCEL_GO_SERVICE_URL": "http://localhost:8081",
        "CODE_EXECUTION_TIMEOUT": "30"
      }
    }
  }
}

You have a high-performance MCP server for Excel file processing, analysis, and visualization. It runs multiple specialized tools across Python and Go engines, supports secure code execution, formula parsing, data quality checks, and rich visualization, and is designed for enterprise-grade reliability and scalability.

How to use

Connect your MCP client to the server to perform Excel data workflows end-to-end. You can explore file metadata, read and transform data, run code that processes Excel data safely, create charts, validate data quality, and compute complex Excel formulas with dependency analysis. Start with a simple read of an Excel file, then layer in cleaning, analysis, visualization, and formula calculations as needed for your tasks.

How to install

Prerequisites: you need Python 3.11+ installed on your system. You may also optionally use Go for the high-performance Excel engine.

Step 1. Set up a virtual environment and install dependencies.

Step 2. Start the MCP server (basic) using the standard server script.

# Create and activate a virtual environment
python3 -m venv venv
source venv/bin/activate  # macOS/Linux
# Windows users: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Start the standard MCP server
python server.py

Security and operation notes

Operate all code execution within a sandboxed environment and enforce strict security checks for formulas. Configure access controls, audit logs, and resource limits to protect your system when running user-provided code or processing sensitive data.

Examples and typical workflows

Common workflows include reading Excel metadata, selecting optimal read parameters, performing data cleaning and transformation, running analysis code, producing interactive charts, validating data quality, and computing Excel formulas with dependency analysis.

Available tools

read_metadata

CSV file metadata reading and intelligent analysis with encoding detection, delimiter recognition, and data statistics

read_excel_metadata

Excel file metadata reading and integrity validation across multiple sheets

excel_read_enhanced

Enhanced Excel reading with Go engine integration and smart parameter suggestion

excel_info_enhanced

Enhanced Excel file information retrieval with sheet counts and structural statistics

run_excel_code

Excel code execution engine in a safe sandbox with complex parameter support and pandas integration

run_code

CSV data code execution engine with安全环境 and data processing capabilities

excel_write_enhanced

Enhanced Excel writing with formatting and style support

excel_chart_enhanced

Internal chart generation for Excel with multiple chart types and styles

excel_performance_comparison

Performance benchmarking between Go and Python Excel processing paths

batch_data_verification_tool

Batch validation across multiple Excel files with concurrent processing

bar_chart_to_html

Interactive bar chart generation using Chart.js for HTML output

pie_chart_to_html

Interactive pie chart generation with animation and data labels

line_chart_to_html

Interactive line chart generation for trend analysis

verify_data_integrity

Data integrity verification and reconciliation with detailed reports

validate_data_quality

Data quality assessment with recommendations and scoring

comprehensive_data_verification_tool

Comprehensive data validation and quality assurance across datasets

enhanced_data_quality_check

Advanced, multi-stage data quality checks with deep analysis

extract_cell_content_advanced

Advanced extraction of cell content with multi-type formatting

convert_character_formats

Automated character format conversion with configurable rules

extract_multi_condition_data

Multi-condition data extraction with flexible filtering

merge_multiple_tables

Smart merging of multiple tables with relationship handling

clean_excel_data

Excel data cleaning to improve quality and consistency

batch_process_excel_files

Parallel batch processing of Excel files with unified configuration

parse_formula

Excel formula parser with AST construction and security checks

compile_workbook

Workbook compiler with code generation and dependency analysis

execute_formula

Safe execution of Excel formulas with context support

analyze_dependencies

Formula dependency analysis and impact assessment

validate_formula

Formula safety and syntax validation with risk assessment