Slander MCP

An MCP server that finds humorous roasts, jokes, and memes about any character (real or fictional) by searching Twitter/X. Uses social proof (engagement metrics) to rank content, with targeted LLM involvement for query generation, batch quality assessment, and nickname extraction.

Installation

npm install
npm run build

Related MCP server: twitter-voice-mcp

Configuration

Create a .env file with:

# Twitter/X API credentials (required)
TWITTER_BEARER_TOKEN=your_twitter_bearer_token_here

# LLM API key (at least one required)
ANTHROPIC_API_KEY=your_anthropic_api_key_here
# or
OPENAI_API_KEY=your_openai_api_key_here

Usage with Claude Desktop

Add to your Claude Desktop MCP configuration:

{
  "mcpServers": {
    "slander": {
      "command": "node",
      "args": ["/path/to/slander_mcp/dist/index.js"],
      "env": {
        "TWITTER_BEARER_TOKEN": "your_token",
        "ANTHROPIC_API_KEY": "your_key"
      }
    }
  }
}

Tools

generate_search_query

Generate effective Twitter search queries for finding slander about a target.

Input:

• target (string, required): Name of character to search for

Output:

• queries: Array of search query strings

Example:

Input:  { "target": "LeBron James" }
Output: { "queries": ["LeBron James ratio", "LeChoke", "LeBron hairline", ...] }

fetch_posts

Fetch posts from Twitter for a given query, looping until quality threshold is met.

Input:

• query (string, required): Search query

• loop_limit (number, optional): Max fetch iterations (default: 5)

• count (number, optional): Posts per fetch (default: 10)

• target (string, optional): Target name for quality evaluation

Output:

• posts: Array of post objects with engagement metrics

• iterations: Number of fetch loops

• stopped_reason: "quality_threshold" or "loop_limit"

rank_posts

Rank fetched posts by engagement, separate text from media, extract nicknames.

Input:

• posts (array, required): Posts from fetch_posts

• top_n (number, optional): Results per category (default: 3)

• target (string, optional): Target name for nickname extraction

Output:

• text_posts: Top text posts ranked by engagement

• media_posts: Top media posts ranked by engagement

• nicknames: Extracted nicknames/slang for the target

Engagement Score Formula:

score = (likes * 1.0) + (retweets * 2.0) + (replies * 0.5)

Example Workflow

1. Generate search queries:

   generate_search_query({ target: "LeBron James" })

2. Fetch posts for each query:

   fetch_posts({ query: "LeChoke", target: "LeBron James" })

3. Combine and rank results:

   rank_posts({ posts: [...all_posts], top_n: 5, target: "LeBron James" })

License

MIT