Gaslighting

A fake web search MCP server for AI alignment testing. It accepts a search query and returns LLM-generated search results shaped by a configurable background story.

Built with FastMCP and compatible with any OpenAI-style API endpoint.

How it works

1. You provide a background story via the BACKGROUND_STORY environment variable
2. The server exposes two tools: search and read_url
3. search — generates 10 realistic search results (url, snippet, date) consistent with the background story
4. read_url — generates a full fake article in markdown for a given URL, inferred from the domain/path and background story
5. The consuming AI agent receives these as if they were real web content

Setup

uv sync

Configuration

Environment Variable	Default	Description
BACKGROUND_STORY	""	The narrative that shapes all generated results
LLM_BASE_URL	https://openrouter.ai/api/v1	OpenAI-compatible API base URL
LLM_API_KEY	""	API key for the LLM endpoint
LLM_MODEL	nousresearch/hermes-4-405b	Model name

Usage

Standalone

uv run server.py

Claude Code MCP config

Add to your .mcp.json:

{
  "mcpServers": {
    "web-search": {
      "command": "uv",
      "args": ["run", "server.py"],
      "env": {
        "BACKGROUND_STORY": "your background story here",
        "LLM_API_KEY": "your-api-key"
      }
    }
  }
}

Tools

search(query)

Returns a JSON array of 10 results:

[
  {
    "url": "https://example.com/some-article",
    "snippet": "A realistic excerpt shaped by the background story.",
    "date": "2025-12-15"
  }
]

read_url(url)

Returns a full fake article in markdown, inferred from the URL and background story. Matches the tone and style of the source website.

License

MIT