parquet_mcp_server

A powerful MCP (Model Control Protocol) server that provides tools for manipulating and analyzing Parquet files. This server is designed to work with Claude Desktop and offers five main functionalities:

1. Text Embedding Generation: Convert text columns in Parquet files into vector embeddings using Ollama models
2. Parquet File Analysis: Extract detailed information about Parquet files including schema, row count, and file size
3. DuckDB Integration: Convert Parquet files to DuckDB databases for efficient querying and analysis
4. PostgreSQL Integration: Convert Parquet files to PostgreSQL tables with pgvector support for vector similarity search
5. Markdown Processing: Convert markdown files into chunked text with metadata, preserving document structure and links

This server is particularly useful for:

• Data scientists working with large Parquet datasets
• Applications requiring vector embeddings for text data
• Projects needing to analyze or convert Parquet files
• Workflows that benefit from DuckDB's fast querying capabilities
• Applications requiring vector similarity search with PostgreSQL and pgvector

Installation

Installing via Smithery

To install Parquet MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @DeepSpringAI/parquet_mcp_server --client claude

Clone this repository

git clone ...
cd parquet_mcp_server

Create and activate virtual environment

uv venv
.venvScriptsactivate  # On Windows
source .venv/bin/activate  # On macOS/Linux

Install the package

uv pip install -e .

Environment

Create a .env file with the following variables:

EMBEDDING_URL=  # URL for the embedding service
OLLAMA_URL=    # URL for Ollama server
EMBEDDING_MODEL=nomic-embed-text  # Model to use for generating embeddings

# PostgreSQL Configuration
POSTGRES_DB=your_database_name
POSTGRES_USER=your_username
POSTGRES_PASSWORD=your_password
POSTGRES_HOST=localhost
POSTGRES_PORT=5432

Usage with Claude Desktop

Add this to your Claude Desktop configuration file (claude_desktop_config.json):

{
  "mcpServers": {
    "parquet-mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "/home/${USER}/workspace/parquet_mcp_server/src/parquet_mcp_server",
        "run",
        "main.py"
      ]
    }
  }
}

Available Tools

The server provides five main tools:

1. Embed Parquet: Adds embeddings to a specific column in a Parquet file

   • Required parameters:
     • input_path: Path to input Parquet file
     • output_path: Path to save the output
     • column_name: Column containing text to embed
     • embedding_column: Name for the new embedding column
     • batch_size: Number of texts to process in each batch (for better performance)

2. Parquet Information: Get details about a Parquet file

   • Required parameters:
     • file_path: Path to the Parquet file to analyze

3. Convert to DuckDB: Convert a Parquet file to a DuckDB database

   • Required parameters:
     • parquet_path: Path to the input Parquet file
   • Optional parameters:
     • output_dir: Directory to save the DuckDB database (defaults to same directory as input file)

4. Convert to PostgreSQL: Convert a Parquet file to a PostgreSQL table with pgvector support

   • Required parameters:
     • parquet_path: Path to the input Parquet file
     • table_name: Name of the PostgreSQL table to create or append to

5. Process Markdown: Convert markdown files into structured chunks with metadata

   • Required parameters:
     • file_path: Path to the markdown file to process
     • output_path: Path to save the output parquet file
   • Features:
     • Preserves document structure and links
     • Extracts section headers and metadata
     • Memory-optimized for large files
     • Configurable chunk size and overlap

Example Prompts

Here are some example prompts you can use with the agent:

For Embedding:

"Please embed the column 'text' in the parquet file '/path/to/input.parquet' and save the output to '/path/to/output.parquet'. Use 'embeddings' as the final column name and a batch size of 2"

For Parquet Information:

"Please give me some information about the parquet file '/path/to/input.parquet'"

For DuckDB Conversion:

"Please convert the parquet file '/path/to/input.parquet' to DuckDB format and save it in '/path/to/output/directory'"

For PostgreSQL Conversion:

"Please convert the parquet file '/path/to/input.parquet' to a PostgreSQL table named 'my_table'"

For Markdown Processing:

"Please process the markdown file '/path/to/input.md' and save the chunks to '/path/to/output.parquet'"

Testing the MCP Server

The project includes a comprehensive test suite in the src/tests directory. You can run all tests using:

python src/tests/run_tests.py

Or run individual tests:

# Test embedding functionality
python src/tests/test_embedding.py

# Test parquet information tool
python src/tests/test_parquet_info.py

# Test DuckDB conversion
python src/tests/test_duckdb_conversion.py

# Test PostgreSQL conversion
python src/tests/test_postgres_conversion.py

# Test Markdown processing
python src/tests/test_markdown_processing.py

You can also test the server using the client directly:

from parquet_mcp_server.client import (
    convert_to_duckdb, 
    embed_parquet, 
    get_parquet_info, 
    convert_to_postgres,
    process_markdown_file  # New markdown processing function
)

# Test DuckDB conversion
result = convert_to_duckdb(
    parquet_path="input.parquet",
    output_dir="db_output"
)

# Test embedding
result = embed_parquet(
    input_path="input.parquet",
    output_path="output.parquet",
    column_name="text",
    embedding_column="embeddings",
    batch_size=2
)

# Test parquet information
result = get_parquet_info("input.parquet")

# Test PostgreSQL conversion
result = convert_to_postgres(
    parquet_path="input.parquet",
    table_name="my_table"
)

# Test markdown processing
result = process_markdown_file(
    file_path="input.md",
    output_path="output.parquet"
)

Troubleshooting

1. If you get SSL verification errors, make sure the SSL settings in your .env file are correct
2. If embeddings are not generated, check:
   • The Ollama server is running and accessible
   • The model specified is available on your Ollama server
   • The text column exists in your input Parquet file
3. If DuckDB conversion fails, check:
   • The input Parquet file exists and is readable
   • You have write permissions in the output directory
   • The Parquet file is not corrupted
4. If PostgreSQL conversion fails, check:
   • The PostgreSQL connection settings in your .env file are correct
   • The PostgreSQL server is running and accessible
   • You have the necessary permissions to create/modify tables
   • The pgvector extension is installed in your database

API Response Format

The embeddings are returned in the following format:

{
    "object": "list",
    "data": [{
        "object": "embedding",
        "embedding": [0.123, 0.456, ...],
        "index": 0
    }],
    "model": "llama2",
    "usage": {
        "prompt_tokens": 4,
        "total_tokens": 4
    }
}

Each embedding vector is stored in the Parquet file as a NumPy array in the specified embedding column.

The DuckDB conversion tool returns a success message with the path to the created database file or an error message if the conversion fails.

The PostgreSQL conversion tool returns a success message indicating whether a new table was created or data was appended to an existing table.

The markdown chunking tool processes markdown files into chunks and saves them as a Parquet file with the following columns:

• text: The text content of each chunk
• metadata: Additional metadata about the chunk (e.g., headers, section info)

The tool returns a success message with the path to the created Parquet file or an error message if the processing fails.