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MCP Mix Server

MCP Mix Server

by mlstudios-ai

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.

source .venv/bin/activate

uv --directory . run mcp_server/main.py

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

{
  "mcpServers": {
    "mcp-mix-server": {
      "command": "uv",
      "args": [
        "--directory",
        "{ABSOLUTE_PATH}/mcp-mix-server",
        "run",
        "mcp_server/main.py"
      ]
    }
  }
}

Verify server registery

  1. 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?”

Related MCP server: Excel MCP Server

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.

ANTHROPIC_API_KEY=<your_api_access_key>

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:

uv run mcp_client/client.py mcp_server/main.py

Test with the following queries:

  • “Summarize the CSV file named sample.csv.”

  • “How many rows are in sample.parquet?”

  • or type "quit" to exit

Available Tools

2 tools
summarize_csv_fileA

Summarise a CVS file by reporting its number of rows and columns.

Args: filename (str): Name of the CSV file in the /data directory.

Returns: str: A string describing the file's dimensions.

Inputs: filename (required). Returns: result

summarize_parquet_fileA

Summarise a Parquet file by reporting its number of rows and columns.

Args: filename (str): Name of the Parquet file in the /data directory.

Returns: str: A string describing the file's dimensions.

Inputs: filename (required). Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A3.9/5.0

    Scored across 2 tools

    Disambiguation5/5

    The two tools have clearly distinct purposes: one handles CSV files and the other handles Parquet files. There is no overlap or ambiguity in their functionality, as each is specialized for a different file format.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'summarize' as the verb and the file type as the noun (csv_file, parquet_file). The naming is perfectly uniform and predictable across the set.

    Tool Count2/5

    With only 2 tools, the server feels thin for a 'Mix Server' that implies broader data processing capabilities. The scope is limited to summarizing two specific file formats, which may not justify the server's name or provide comprehensive coverage for data analysis tasks.

    Completeness2/5

    The tool surface is severely incomplete for data processing. It only offers summarization for CSV and Parquet files, lacking essential operations like reading, writing, filtering, transforming, or handling other common formats (e.g., JSON, Excel). This will likely cause agent failures in broader workflows.

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