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yzfly

Excel MCP Server

by yzfly
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Python
Hybrid

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Server capabilities have not been inspected yet.

Tools

Functions exposed to the LLM to take actions

NameDescription
read_excelA
Read an Excel file and return its contents as a string.

Args:
    file_path: Path to the Excel file
    sheet_name: Name of the sheet to read (only for .xlsx, .xls)
    nrows: Maximum number of rows to read
    header: Row to use as header (0-indexed)
    
Returns:
    String representation of the Excel data
write_excelB
Write data to an Excel file.

Args:
    file_path: Path to save the Excel file
    data: Data in CSV or JSON format
    sheet_name: Name of the sheet (for Excel files)
    format: Format of the input data ('csv' or 'json')
    
Returns:
    Confirmation message
update_excelC
Update an existing Excel file with new data.

Args:
    file_path: Path to the Excel file to update
    data: New data in CSV or JSON format
    sheet_name: Name of the sheet to update (for Excel files)
    format: Format of the input data ('csv' or 'json')
    
Returns:
    Confirmation message
analyze_excelC
Perform statistical analysis on Excel data.

Args:
    file_path: Path to the Excel file
    columns: Comma-separated list of columns to analyze (analyzes all numeric columns if None)
    sheet_name: Name of the sheet to analyze (for Excel files)
    
Returns:
    JSON string with statistical analysis
filter_excelB
Filter Excel data using a pandas query string.

Args:
    file_path: Path to the Excel file
    query: Pandas query string (e.g., "Age > 30 and Department == 'Sales'")
    sheet_name: Name of the sheet to filter (for Excel files)
    
Returns:
    Filtered data as string
pivot_tableB
Create a pivot table from Excel data.

Args:
    file_path: Path to the Excel file
    index: Column to use as the pivot table index
    columns: Optional column to use as the pivot table columns
    values: Column to use as the pivot table values
    aggfunc: Aggregation function ('mean', 'sum', 'count', etc.)
    sheet_name: Name of the sheet to pivot (for Excel files)
    
Returns:
    Pivot table as string
export_chartB
Create a chart from Excel data and return as an image.

Args:
    file_path: Path to the Excel file
    x_column: Column to use for x-axis
    y_column: Column to use for y-axis
    chart_type: Type of chart ('line', 'bar', 'scatter', 'hist')
    sheet_name: Name of the sheet to chart (for Excel files)
    
Returns:
    Chart as image
data_summaryC
Generate a comprehensive summary of the data in an Excel file.

Args:
    file_path: Path to the Excel file
    sheet_name: Name of the sheet to summarize (for Excel files)
    
Returns:
    Comprehensive data summary as string

Prompts

Interactive templates invoked by user choice

NameDescription
analyze_excel_data Create a prompt for analyzing Excel data
create_chart Create a prompt for generating charts from Excel data
data_cleaning Create a prompt for cleaning and preprocessing Excel data

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.4/5.0

Scored across 8 tools

Disambiguation4/5

Most tools have distinct purposes like reading, writing, filtering, and analyzing Excel data, but analyze_excel and data_summary overlap significantly as both perform data analysis/summarization. The descriptions help differentiate them slightly, but an agent might struggle to choose between them for basic statistical tasks.

Naming Consistency5/5

All tools follow a consistent verb_noun or verb_excel pattern (e.g., read_excel, update_excel, filter_excel, pivot_table). The naming is predictable and readable throughout, with no mixing of conventions like camelCase or snake_case deviations.

Tool Count5/5

With 8 tools, this server is well-scoped for Excel operations, covering core tasks like reading, writing, updating, filtering, analyzing, summarizing, charting, and pivoting. Each tool earns its place without feeling excessive or insufficient for the domain.

Completeness4/5

The toolset provides strong coverage for Excel data manipulation and analysis, including CRUD-like operations (read, write, update) and advanced features (pivot tables, charts). A minor gap exists in lacking tools for specific Excel formatting or cell-level edits, but agents can work around this with the available tools.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

Resources