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DeepSpringAI

parquet mcp server

by DeepSpringAI
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Python
Remote

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:

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
.venv\Scripts\activate  # 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=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

Related MCP server: my-mcp-server

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 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:

python src/tests/run_tests.py

Or run individual tests:

# 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:

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

PostgreSQL Function for Vector Similarity Search

To perform vector similarity searches in PostgreSQL, you can use the following function:

-- Create the function for vector similarity search
CREATE OR REPLACE FUNCTION match_web_search(
  query_embedding vector(1024),  -- Adjusted vector size
  match_threshold float,
  match_count int  -- User-defined limit for number of results
)
RETURNS TABLE (
  id bigint,
  metadata jsonb,
  text TEXT,  -- Added text column to the result
  date TIMESTAMP,  -- Using the date column instead of created_at
  similarity float
)
LANGUAGE plpgsql
AS $$
BEGIN
  RETURN QUERY
  SELECT
    web_search.id,
    web_search.metadata,
    web_search.text,  -- Returning the full text of the chunk
    web_search.date,  -- Returning the date timestamp
    1 - (web_search.embedding <=> query_embedding) as similarity
  FROM web_search
  WHERE 1 - (web_search.embedding <=> query_embedding) > match_threshold
  ORDER BY web_search.date DESC,  -- Sort by date in descending order (newest first)
           web_search.embedding <=> query_embedding  -- Sort by similarity
  LIMIT match_count;  -- Limit the results to the match_count specified by the user
END;
$$;

This function allows you to perform similarity searches on vector embeddings stored in a PostgreSQL database, returning results that meet a specified similarity threshold and limiting the number of results based on user input. The results are sorted by date and similarity.

Postgres table creation

CREATE TABLE web_search (
    id SERIAL PRIMARY KEY,
    text TEXT,
    metadata JSONB,
    embedding VECTOR(1024),

    -- This will be auto-updated
    date TIMESTAMP DEFAULT NOW()
);

Available Tools

2 tools
search-webC

Perform a web search and scrape results

ParametersJSON Schema
NameRequiredDescriptionDefault
queriesYesList of search queries
page_numberNoPage number for the search results

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must disclose behavioral traits. It states that results are scraped but provides no details on output format, pagination behavior, rate limits, or error handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence with no extraneous information, making it efficiently front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Without an output schema or annotations, the description is insufficient for a web search tool. It does not explain return values, result structure, or how scraping integrates with search, leaving significant gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents both parameters. The description adds no additional meaning beyond the parameter descriptions, meeting the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action: perform a web search and scrape results. It uses a specific verb-resource pair, but does not explicitly differentiate from the sibling tool 'extract-info-from-search'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus the sibling tool 'extract-info-from-search' or in what contexts it is appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updatesv1.0.0
    • First observedextract-info-from-search
    • First observedsearch-web
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TDQS

C2.5/5.0
Disambiguation4/5

The two tools have clearly distinct purposes: one for performing web searches and scraping results, the other for extracting information from those prior searches. While they are related, there is no functional overlap.

Naming Consistency5/5

Both tool names follow a consistent snake_case verb_noun pattern. 'search-web' and 'extract-info-from-search' both clearly indicate action and target.

Tool Count3/5

With only 2 tools, the server feels minimal for a web search and extraction domain. However, the tools cover a basic two-step workflow, making the count borderline acceptable.

Completeness3/5

The server provides a basic search and extraction cycle but lacks obvious features like managing search history, caching, or supporting different output formats. Minor gaps exist.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

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