Skip to main content

Reddit MCP Server

Project description

Reddit MCP Server

This repository contains a Model Context Protocol server implementation for Reddit that allows AI assistants to access and interact with Reddit content through PRAW (Python Reddit API Wrapper).

Reddit Server MCP server

image

What is MCP?

The Model Context Protocol (MCP) is a standard for enabling AI assistants to interface with external services, tools, and data sources. This server implements the MCP specification to provide access to Reddit content.

To know more about MCP, Check this video

Features

  • Get detailed user information with engagement analysis
  • Fetch and analyze top posts from any subreddit
  • Get comprehensive subreddit statistics and health metrics
  • View trending subreddits with growth patterns
  • Create strategic posts with timing recommendations
  • Reply to posts and comments with engagement optimization
  • AI-driven insights and recommendations
  • Smart response formatting with engagement metrics

Installation

  1. Clone this repository
git clone https://github.com/Arindam200/reddit-mcp.git
cd reddit-mcp
  1. Connect to the MCP server

    Copy the below json with the appropriate {{PATH}} values:

    {
      "mcpServers": {
        "reddit": {
          "command": "{{PATH_TO_UV}}", // Run `which uv` and place the output here
          "args": [
            "--directory",
            "{{PATH_TO_SRC}}", // cd into the repo, run `pwd` and enter the output here
            "run",
            "server.py"
          ],
          "env": {
            "REDDIT_CLIENT_ID": "your_client_id",
            "REDDIT_CLIENT_SECRET": "your_client_secret",
            "REDDIT_USERNAME": "your_username", // Optional for authenticated operations
            "REDDIT_PASSWORD": "your_password" // Optional for authenticated operations
          }
        }
      }
    }
    

    You can obtain Reddit API credentials by creating an app at Reddit's app preferences page.

    For Claude, save this as claude_desktop_config.json in your Claude Desktop configuration directory at:

    ~/Library/Application Support/Claude/claude_desktop_config.json
    

    For Cursor, save this as mcp.json in your Cursor configuration directory at:

    ~/.cursor/mcp.json
    
  2. Restart Claude Desktop / Cursor

    Open Claude Desktop and you should now see Reddit as an available integration.

    Or restart Cursor.

Available Tools

The server provides the following tools:

Read-only Tools (require only client credentials):

  • get_user_info(username) - Get detailed user analysis with engagement insights
  • get_top_posts(subreddit, time_filter, limit) - Get and analyze top posts
  • get_subreddit_stats(subreddit) - Get comprehensive subreddit analysis
  • get_trending_subreddits() - Get list of trending subreddits
  • get_submission_by_url(url) - Get a Reddit submission by its URL
  • get_submission_by_id(submission_id) - Get a Reddit submission by its ID

Authenticated Tools (require user credentials):

  • who_am_i() - Get information about the currently authenticated user
  • create_post(subreddit, title, content, flair, is_self) - Create an optimized post
  • reply_to_post(post_id, content, subreddit) - Add a reply with engagement insights
  • reply_to_comment(comment_id, content, subreddit) - Add a strategic reply

Example Queries

Here are some examples of what you can ask an AI assistant connected to this server:

  • "Who am I on Reddit?" or "Show my Reddit profile"
  • "Analyze u/spez's Reddit activity"
  • "Show me the top posts from r/Python this week"
  • "Get statistics about r/AskReddit"
  • "What are the trending subreddits right now?"
  • "Create a post in r/Python about a new project"
  • "Reply to this post with an insightful comment"
  • "What's the best time to post in this subreddit?"

Advanced Features

AI-Driven Analysis

The server provides intelligent analysis in several areas:

  1. User Analysis

    • Engagement patterns
    • Activity trends
    • Community influence
    • Personalized recommendations
  2. Post Analysis

    • Performance metrics
    • Engagement quality
    • Timing optimization
    • Content impact assessment
  3. Community Analysis

    • Health indicators
    • Growth patterns
    • Activity metrics
    • Engagement opportunities

Smart Response Formatting

  • Organized bullet points
  • Engagement statistics
  • AI-driven insights
  • Strategic recommendations
  • Performance metrics

Authentication

The server supports two levels of authentication:

  1. Read-only Access

    • Requires: client_id and client_secret
    • Allows: Fetching public data, reading posts/comments
  2. Authenticated Access

    • Requires: All read-only credentials PLUS username and password
    • Allows: All read-only operations PLUS posting and commenting

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

reddit_mcp_dark-0.1.2.tar.gz (18.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

reddit_mcp_dark-0.1.2-py3-none-any.whl (16.2 kB view details)

Uploaded Python 3

File details

Details for the file reddit_mcp_dark-0.1.2.tar.gz.

File metadata

  • Download URL: reddit_mcp_dark-0.1.2.tar.gz
  • Upload date:
  • Size: 18.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.5.29

File hashes

Hashes for reddit_mcp_dark-0.1.2.tar.gz
Algorithm Hash digest
SHA256 252dd4ab6ac6076ad48ce95e36aab99ffbe93b3f546d61e0e5289660d84a7c0b
MD5 fc004942c31d01cb1c6f56e35214e528
BLAKE2b-256 8483e232c59aa4185a243fa138ed8eba8bb462b37b104a90f76a135837211eb3

See more details on using hashes here.

File details

Details for the file reddit_mcp_dark-0.1.2-py3-none-any.whl.

File metadata

File hashes

Hashes for reddit_mcp_dark-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 1547b36f881b7c5b6f9adafb2c7e7a0187003b37a7b32f147d93052bf52d3813
MD5 20acb3cb92f668dacbdac89e320aa91a
BLAKE2b-256 c9c3946fe5ec0ddee0ed3112504621d110fb643fd52882d396ee4d8afbbb7154

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page