The MCP Mix Server provides tools to summarize CSV and Parquet files by reporting their dimensions (number of rows and columns). Simply provide a filename as input to get a string describing the file's structure.
MCP Server/Client Example
The server is a modified implementation following tutorial from Medium by Alex Merced.
The custom client is an implementation from Anthropic quickstart tutorial Build an MCP Client.
MCP Server
Navigate to your project directory, run these commands in the terminal.
NOTE: There are no outputs from the terminal - it's normal.
MCP Client
Claude Desktop
On MacOS or Linx, add the following entry to ~/Library/Application Support/Claude/claude_desktop_config.json
For Windows, add the following entry to %APPDATA%\Claude\claude_desktop_config.json
Verify server registery
- Click on "Searches and tools" option.
2. Click on "mcp-mix-server", you should see the listed tools 3. Test with the following queries: - “Summarize the CSV file named sample.csv.” - “How many rows are in sample.parquet?”
Custom MCP client
Alternatively, use a custom client in mcp_client/ implemented following the Anthropic quickstart tutorial Build an MCP Client.
Create a .env file in the root folder and put your Athropic API access key in there. To obtain the API access key, login your Anthropic account and following instructions.
You can always use your own reasoning model. But this repo is very basic to demonstrate how MCP Server/Client works so we stick with Claude.
When starting the client, it will automatically start the server in stdio transport mode. This means the client access the server locally, not remotely, which uses SSE transport mode. There is no need run the server script separately.
To run the custom client:
Test with the following queries:
- “Summarize the CSV file named sample.csv.”
- “How many rows are in sample.parquet?”
- or type "quit" to exit
local-only server
The server can only run on the client's local machine because it depends on local resources.
A tutorial implementation MCP server that enables analysis of CSV and Parquet files. Allows users to summarize data and query file information through natural language interactions.
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