Which integrations are available for this server?

Leverages OpenAI's models to generate effective search queries for social media, evaluate the quality of humor and memes, and assist in nickname extraction for character analysis.

How do I use Slander MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Slander MCP Find the funniest roasts and memes about LeBron James" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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

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

Generate search queries:
generate_search_query({ target: "LeBron James" })
Fetch posts for each query:
fetch_posts({ query: "LeChoke", target: "LeBron James" })
Combine and rank results:
rank_posts({ posts: [...all_posts], top_n: 5, target: "LeBron James" })