Web Eyes

Search, crawl, and summarize the web — exposed as both a REST API and an MCP server for LLM agents.

What it does

Web Eyes provides a pipeline of web intelligence tools:

Search — query SearXNG for web results
Crawl — extract clean text from URLs using a headless browser
Summarize — distill content via an LLM (NVIDIA NIM)
Ask — full pipeline: search → crawl → synthesize an answer with citations
See — take screenshots and use a vision LLM to extract content from JS-heavy, canvas-rendered, or image-heavy pages
Look — analyze any image directly via vision AI (no URL crawling needed)

These are exposed via a FastAPI REST API and an MCP (Model Context Protocol) server, so any MCP-compatible agent (Claude Desktop, Claude Code, Cursor, etc.) can use them directly.

Quick Start

1. Start SearXNG

docker compose up -d

2. Configure environment

cp .env.example .env
# Edit .env and set NIM_API_KEY (get one at https://build.nvidia.com/)

3. Install dependencies

pip install -r requirements.txt

4. Run

REST API + MCP together (port 3000):

python main.py

REST API: http://localhost:3000
MCP endpoint: http://localhost:3000/mcp
Interactive docs: http://localhost:3000/docs

Standalone MCP server:

python run_mcp.py              # stdio (default)
python run_mcp.py http         # streamable-http on port 3001
python run_mcp.py sse          # SSE on port 3001

REST API

Method	Path	Description
`POST`	`/search`	Search → crawl → summarize
`POST`	`/crawl`	Crawl specific URLs
`POST`	`/summarize`	Crawl + summarize specific URLs
`POST`	`/ask`	Search → crawl → answer with citations
`POST`	`/see`	Screenshot + vision extraction + summarize
`POST`	`/look`	Analyze a base64-encoded image with vision AI

Example:

curl -X POST http://localhost:3000/search \
  -H "Content-Type: application/json" \
  -d '{"query": "latest Rust release", "limit": 5}'

MCP Tools

Tool	Parameters	Description
`search_web`	`query`, `limit=10`	Search, crawl, and summarize
`crawl_pages`	`urls`	Extract raw text from URLs
`summarize_pages`	`urls`, `instruction?`	Crawl and summarize URLs
`ask_web`	`question`, `scrape_top=3`	Answer a question with web sources
`see_pages`	`urls`, `instruction?`, `extract_prompt?`	Screenshot + vision extraction + summarize
`look_at_image`	`image_base64`, `instruction?`	Analyze an image directly with vision AI

Agent Configuration

Claude Desktop / Claude Code (mcp.json):

{
  "mcpServers": {
    "web-eyes": {
      "command": "python",
      "args": ["C:\\Users\\you\\web_eyes\\run_mcp.py", "stdio"]
    }
  }
}

Remote agents (HTTP transport):

http://localhost:3001/mcp

Configuration

All settings are in .env. See .env.example for defaults.

Variable	Default	Description
`NIM_API_KEY`	—	NVIDIA NIM API key (required for summarize/ask)
`NIM_BASE_URL`	`https://integrate.api.nvidia.com/v1`	NIM API endpoint
`NIM_MODEL`	`google/gemma-3-27b-it`	LLM model for summarization
`NIM_VISION_MODEL`	`google/gemma-3-27b-it`	Vision model for screenshot extraction
`VISION_FALLBACK_ENABLED`	`true`	Auto-fallback to vision when text extraction fails
`VISION_WORD_THRESHOLD`	`30`	Minimum words before triggering vision fallback
`VISION_MAX_IMAGE_DIMENSION`	`1280`	Max screenshot dimension before resize
`SEARXNG_HOST`	`localhost`	SearXNG host
`SEARXNG_PORT`	`8888`	SearXNG port
`API_HOST`	`0.0.0.0`	REST API bind address
`API_PORT`	`3000`	REST API port
`MCP_HOST`	`0.0.0.0`	Standalone MCP bind address
`MCP_PORT`	`3001`	Standalone MCP port

Project Structure

web_eyes/
├── main.py           FastAPI app (REST + mounted MCP)
├── mcp_server.py     MCP server with 6 tools
├── run_mcp.py        Standalone MCP entry point
├── controller.py     Core pipeline logic
├── search.py         SearXNG search client
├── crawler.py        Crawl4AI web crawler
├── summarizer.py     NIM LLM summarization + vision extraction
├── vision.py         Image resize and message utilities
├── config.py         Environment config
├── logger.py         Rich logging
├── docker-compose.yml
├── requirements.txt
└── searxng/
    └── settings.yml  SearXNG configuration

License

MIT