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"A method for finding people on LinkedIn and organizing them in a CSV" matching MCP tools:

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    Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.
    Last updated
    1
    MIT
  • A
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    A Model Context Protocol server focused on China's A-share stock market that provides data on stocks, financials, market indices, and macroeconomic indicators.
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    MIT

Matching MCP Connectors

  • Extract structured LinkedIn profile data using a reliable cache lookup method to avoid web scraping issues. This tool helps find professional information by searching with first name, last name, and URL parameters.
    MIT
  • Executes a FLOX trading strategy on a CSV dataset in a resource-limited sandbox, returning backtest statistics and stdout. Use to validate strategy code on historical data.
    MIT
  • Search a knowledge graph for entities like people, organizations, or technologies using semantic similarity. Discover who or what is mentioned in memories and find entities by concept.
    MIT
  • Retrieve localized metadata for Teamfight Tactics augments, including names, descriptions, tiers, and image URLs, exported in CSV format. Specify a language code to access data in your preferred language.
    MIT
  • Search audit findings by title, severity, or status to check for duplicates before creating a new finding or to locate a specific finding for update or evidence attachment.
    MIT
  • Create polar line plots from SQL queries on CSV or Parquet data sources. Visualize radial and angular coordinates with optional color coding for multi-dimensional analysis.
    MIT
  • Create box plots from SQL query results on CSV or Parquet data sources to visualize statistical distributions and identify outliers in your data.
    MIT
  • Create bar charts from SQL query results on CSV, Parquet, or database sources to visualize data relationships and trends for analysis.
    MIT
  • Visualize data distribution patterns by creating a 2D histogram from SQL query results. Generate density heatmaps for CSV and Parquet data sources to analyze spatial relationships and concentration areas.
    MIT