search_mcp_server

# parquet_mcp_server [](https://smithery.ai/server/@DeepSpringAI/parquet_mcp_server) A powerful MCP (Model Control Protocol) server that provides tools for performing web searches and finding similar content. This server is designed to work with Claude Desktop and offers two main functionalities: 1. **Web Search**: Perform a web search and scrape results 2. **Similarity Search**: Extract relevant information from previous searches This server is particularly useful for: - Applications requiring web search capabilities - Projects needing to find similar content based on search queries ## Installation ### Installing via Smithery To install Parquet MCP Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@DeepSpringAI/parquet_mcp_server): ```bash npx -y @smithery/cli install @DeepSpringAI/parquet_mcp_server --client claude ``` ### Clone this repository ```bash git clone ... cd parquet_mcp_server ``` ### Create and activate virtual environment ```bash uv venv .venv\Scripts\activate # On Windows source .venv/bin/activate # On macOS/Linux ``` ### Install the package ```bash uv pip install -e . ``` ### Environment Create a `.env` file with the following variables: ```bash EMBEDDING_URL=http://sample-url.com/api/embed # URL for the embedding service OLLAMA_URL=http://sample-url.com/ # URL for Ollama server EMBEDDING_MODEL=sample-model # Model to use for generating embeddings SEARCHAPI_API_KEY=your_searchapi_api_key FIRECRAWL_API_KEY=your_firecrawl_api_key VOYAGE_API_KEY=your_voyage_api_key AZURE_OPENAI_ENDPOINT=http://sample-url.com/azure_openai AZURE_OPENAI_API_KEY=your_azure_openai_api_key ``` ## Usage with Claude Desktop Add this to your Claude Desktop configuration file (`claude_desktop_config.json`): ```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 two main tools: 1. **Search Web**: Perform a web search and scrape results - Required parameters: - `queries`: List of search queries - Optional parameters: - `page_number`: Page number for the search results (defaults to 1) 2. **Extract Info from Search**: Extract relevant information from previous searches - Required parameters: - `queries`: List of search queries to merge ## Example Prompts Here are some example prompts you can use with the agent: ### For Web Search: ``` "Please perform a web search for 'macbook' and 'laptop' and scrape the results from page 1" ``` ### For Extracting Info from Search: ``` "Please extract relevant information from the previous searches for 'macbook'" ``` ## Testing the MCP Server The project includes a comprehensive test suite in the `src/tests` directory. You can run all tests using: ```bash python src/tests/run_tests.py ``` Or run individual tests: ```bash # Test Web Search python src/tests/test_search_web.py # Test Extract Info from Search python src/tests/test_extract_info_from_search.py ``` You can also test the server using the client directly: ```python from parquet_mcp_server.client import ( perform_search_and_scrape, # New web search function find_similar_chunks # New extract info function ) # Perform a web search perform_search_and_scrape(["macbook", "laptop"], page_number=1) # Extract information from the search results find_similar_chunks(["macbook"]) ``` ### 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