Evo Scry

MCP server for internet search via direct Google and DuckDuckGo HTML scraping with AI-powered result normalization.

Python. Zero API keys required for search. Optional AI summarization via GitHub Copilot token or local Ollama model.

Features

• 4 MCP Tools: web_search, search_google, search_duckduckgo, extract_content
• Multi-engine aggregation: Parallel search across Google + DuckDuckGo with deduplication and cross-engine ranking
• AI summarization: Optional result summaries via GitHub Copilot or local Ollama models
• Production-ready: Systemd service with full environment variable configuration
• Privacy-respecting: Direct HTML scraping, no third-party search APIs
• FastMCP pattern: Same architecture as evo-mem — SSE + streamable-http dual transport

Quick Start

# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install
pip install -e .

# Run (STDIO transport for local MCP clients)
python -m evoscry.mcp_server

# Run (SSE transport for remote/systemd)
python -m evoscry.mcp_server --transport sse --port 3000

Claude Desktop / VS Code Configuration

{
  "mcpServers": {
    "evo-scry": {
      "command": "python",
      "args": ["-m", "evoscry.mcp_server"]
    }
  }
}

SSE Transport (remote)

{
  "mcpServers": {
    "evo-scry": {
      "url": "http://your-server:3000/sse"
    }
  }
}

MCP Tools

web_search

Search the internet using both Google and DuckDuckGo. Results are aggregated, deduplicated, and ranked.

Parameter	Type	Required	Default	Description
query	string	yes	—	Search query
engines	list	no	config	Engines to use
max_results	int	no	10	Max results per engine
language	string	no	"en"	Language code
date_range	string	no	—	"day", "week", "month", "year"
summarize	bool	no	false	AI-summarize results

search_google / search_duckduckgo

Engine-specific search tools with the same parameters (minus engines and summarize).

extract_content

Fetch URLs and extract clean text or Markdown content.

Parameter	Type	Required	Default	Description
url	str | list[str]	yes	—	URL(s) to extract
format	string	no	"markdown"	"text" or "markdown"

AI Summarization Setup

GitHub Copilot Token (recommended)

python -m evoscry.generate_copilot_token

Follow the prompts to authenticate via GitHub OAuth Device Flow. Add the generated token to your .env or systemd environment file.

Local Model (Ollama)

# In .env or systemd env:
EVOSCRY_LOCAL_MODEL_URL=http://localhost:11434
EVOSCRY_LOCAL_MODEL_NAME=llama3

Systemd Deployment

# Install (requires root)
sudo bash install.sh

# Configure
sudo vim /etc/evo-scry/evo-scry.env

# Start
sudo systemctl enable --now evo-scry

SSE endpoint: http://localhost:3000/sse Health check: http://localhost:3000/health

Configuration

All options via environment variables. See .env.example for the full list.

Variable	Default	Description
EVOSCRY_TRANSPORT	stdio	stdio, sse, or streamable-http
EVOSCRY_HOST	0.0.0.0	HTTP bind address
EVOSCRY_PORT	3000	HTTP port
EVOSCRY_SEARCH_ENGINES	duckduckgo	Enabled engines
EVOSCRY_MAX_RESULTS	10	Results per engine
EVOSCRY_REQUEST_DELAY_MS	1000	Rate limiting delay
EVOSCRY_USER_AGENT	rotate	UA rotation
EVOSCRY_PROXY_URL	—	HTTP/SOCKS5 proxy
EVOSCRY_COPILOT_TOKEN	—	Copilot API token
EVOSCRY_COPILOT_REFRESH_TOKEN	—	GitHub OAuth token for auto-refresh
EVOSCRY_LOCAL_MODEL_URL	—	Ollama endpoint
EVOSCRY_LOG_LEVEL	info	Log verbosity
EVOSCRY_CACHE_TTL_SECONDS	300	Cache TTL

License

MIT