qanon-mcp-server

qanon-mcp-server

3.3

If you are the rightful owner of qanon-mcp-server and would like to certify it and/or have it hosted online, please leave a comment on the right or send an email to henry@mcphub.com.

An MCP server providing access to a dataset of Q-Anon posts for research purposes.

The Q-Anon Posts/Drops MCP Server is a Model Context Protocol server designed to facilitate access to a comprehensive dataset of Q-Anon posts. This server is particularly useful for anthropological and sociological research, allowing AI assistants like Claude to search, filter, and analyze Q-Anon drops. The dataset is sourced from the JSON-QAnon repository, which provides various formats and schemas for the data. The server is compatible with the Claude Desktop application, enabling seamless integration and interaction with the dataset. Users can perform a variety of operations such as retrieving posts by ID, searching for specific keywords, and generating analytical reports. The server is designed to be used with Python 3.10 or higher and requires the 'uv' package manager for installation and operation.

Features

  • Access to a comprehensive dataset of Q-Anon posts
  • Integration with Claude Desktop for enhanced analysis
  • Ability to search and filter posts by keywords, date, and author
  • Tools for generating word clouds and timelines
  • Detailed post analysis including references and context

Tools

  1. get_post_by_id_tool

    Search for specific posts by ID

  2. search_posts

    Find posts that contain specific keywords or phrases

  3. get_posts_by_date

    Search posts within a specific date range

  4. get_posts_by_author_id

    Find posts with specific author IDs

  5. analyze_post

    Get detailed analysis of posts, including citations and context

  6. get_timeline_summary

    Generate timelines arranged in chronological order

  7. word_cloud_by_post_ids

    Generate word frequency analysis for posts within the specified ID range

  8. word_cloud_by_date_range

    Generate word frequency analysis for posts within the specified date range