
CSV MCP Server
Enables comprehensive CSV file management including creating, editing, analyzing, and transforming CSV data anywhere in the filesystem. Provides statistical analysis, data validation, filtering, and grouping capabilities through MCP protocol over stdio transport.
README
CSV MCP Server
A Model Context Protocol (MCP) server for comprehensive CSV file management using stdio transport exclusively. This server provides tools for creating, editing, analyzing, and managing CSV files using the MCP protocol over standard input/output.
Features
- File Management: Create, read, update, and delete CSV files
- Absolute Path Support: Work with CSV files anywhere in the filesystem using absolute paths
- Data Analysis: Basic statistical analysis and data exploration
- Data Transformation: Filter, sort, group, and transform data
- Data Validation: Check data integrity and format validation
- Import/Export: Support for various CSV formats and encodings
- Stdio Transport: Uses JSON-RPC 2.0 over standard input/output for communication
Installation
uv add csv-mcp-server
Usage
Running the Server
# Using stdio transport (default and only option)
uv run csv-mcp-server
# With custom log level
uv run csv-mcp-server --log-level DEBUG
# Development mode
uv run mcp dev csv_mcp_server/server.py
Available Tools
create_csv
: Create a new CSV file with headers and initial datacreate_csv_at_path
: Create a CSV file at a specific absolute or relative pathread_csv
: Read and display CSV file contentsupdate_csv
: Update specific cells or rows in a CSV filedelete_csv
: Delete a CSV fileadd_row
: Add new rows to an existing CSV fileremove_row
: Remove specific rows from a CSV fileget_info
: Get basic information about a CSV fileget_statistics
: Get statistical summary of numeric columnsfilter_data
: Filter CSV data based on conditionssort_data
: Sort CSV data by specified columnsgroup_data
: Group and aggregate CSV datavalidate_data
: Validate CSV data integrity and formatget_path_info
: Get detailed information about a file path (supports absolute paths)
Available Resources
csv://{filename}
: Access CSV file contents as a resourcecsv-info://{filename}
: Get metadata about a CSV file
Available Prompts
analyze_csv
: Generate analysis prompts for CSV datatransform_csv
: Generate transformation suggestions
Configuration
The server can be configured with environment variables:
CSV_STORAGE_PATH
: Base path for CSV file storage (default: current directory)CSV_MAX_FILE_SIZE
: Maximum file size in MB (default: 50)CSV_BACKUP_ENABLED
: Enable automatic backups (default: true)CSV_SUPPORT_ABSOLUTE_PATHS
: Enable absolute path support (default: true)
Absolute Path Support
The CSV MCP server now supports working with CSV files anywhere in the filesystem using absolute paths. This feature allows you to:
- Create CSV files in any accessible directory
- Read and modify existing CSV files from anywhere on the system
- Work with files outside the default storage directory
- Maintain backward compatibility with relative paths
Security Features
- Path Validation: Automatically validates absolute paths for safety
- System Directory Protection: Prevents access to critical system directories
- Permission Checking: Verifies directory and file access permissions
- Symlink Resolution: Safely resolves symbolic links to prevent path traversal attacks
Usage Examples
# Create a CSV file at an absolute path
create_csv_at_path(
filepath="/path/to/your/data/sales.csv",
headers=["Date", "Product", "Sales"],
data=[["2024-01-01", "Laptop", 1200]]
)
# Get information about any file path
get_path_info(filepath="/path/to/your/file.csv")
# All existing tools work with absolute paths
read_csv("/path/to/your/data/analysis.csv")
update_csv("/path/to/your/data/analysis.csv", row_index=0, column="Sales", value=1500)
Transport
This server exclusively uses stdio transport with JSON-RPC 2.0 protocol, making it ideal for:
- Integration with MCP clients that support stdio transport
- Command-line tools and scripts
- Development and testing environments
- Containerized deployments
Examples
See the examples/
directory for usage examples with various MCP clients:
demo_client.py
: Basic MCP client demonstrationsales_analysis.py
: Sales data analysis exampleabsolute_path_demo.py
: Demonstration of absolute path functionality
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.