Skip to main content
Glama
130,083 tools. Last updated 2026-05-07 00:40

"Techniques and Tools for Data Analysis, Exploration, and Working with Parquet and CSV Files" matching MCP tools:

  • Upload files from your host system to the REMnux malware analysis environment for examination. Transfer files up to 200MB to the samples directory for use with analysis tools.
    GPL 3.0
  • Identify file types and get recommended REMnux malware analysis tools to plan investigation strategies before execution.
    GPL 3.0
  • Analyze suspicious files for malware using REMnux tools. Detects file type automatically and runs appropriate analysis tools with configurable depth levels for triage or comprehensive investigation.
    GPL 3.0
  • Saves Chronulus forecast data and explanations to CSV and TXT files for analysis and reference, with optional rescaling capabilities.
    Python
    MIT
  • Load and analyze local or remote data files by providing an absolute path or URL. Supports CSV, JSON, HTML, Excel, ODS, and Parquet formats. Returns DataFrame structure and metadata for quick data understanding.

Matching MCP Servers

Matching MCP Connectors

  • Extract data from an Excel sheet and convert it to CSV text. Provide base64-encoded file, optional sheet index (0-based), and delimiter. Returns CSV content for easy data processing.
  • Uploads failure data CSV to identify top failure modes, correlate with production lots, and provide actionable recommendations for quality improvement.
    Apache 2.0
  • Convert PDF and scanned documents to CSV files, preserving layout and table structure for data extraction.
    MIT
  • Export historical on-chain derivatives data (trades, fills, orderbooks, etc.) as CSV or Parquet by event type and optional market filter.
    MIT
  • Send input to a running process and automatically receive the response. Essential for local file analysis (CSV, JSON, data processing) using REPLs like Python, avoiding tools that cannot access files.
  • Export ENCODE experiment data as structured tables (CSV, TSV, JSON) for analysis in Excel, R, pandas, or sharing with collaborators. Includes metadata, publication counts, and file information.
  • Extract tables from Markdown documents and convert them to CSV format. Parses GFM pipe-tables to output comma-separated values for data analysis and export.
    MIT
  • Retrieve the schema, sample data, and row count of a parquet file to analyze its structure before querying. Ideal for understanding columns, data types, and preparing SQL queries efficiently.
    MIT
  • Retrieve a list of available Parquet files for SQL queries on Cryo MCP Server. Each entry includes file paths, dataset types, and metadata to use in SQL queries with query_sql().
    MIT
  • Retrieve random document samples from MongoDB collections for data exploration, testing, and analysis in JSON or CSV formats.
    MIT
  • Read and filter exported crawl data from Screaming Frog SEO Spider. Access CSV files with pagination and column filtering for analysis.
    MIT
  • Convert CSV data to typed JSON with automatic delimiter detection, header parsing, and value type casting for processing spreadsheet exports.
    MIT
  • Start terminal processes to analyze local files like CSV, JSON, and logs, using Python or Node.js REPLs for data processing when the analysis tool cannot access local files.