Deep Search Engine MCP Server

Free, Open-Source Search Engine MCP Server — 7 sources, 10 consolidated tools, semantic search via ChromaDB, zero cost.

Features

• 7 Data Sources: Web, Reddit, YouTube, GitHub, Twitter/X, DuckDuckGo, Wikipedia

• 10 Consolidated Tools: All features preserved, combined by mode/action parameters

• Semantic Search: ChromaDB + sentence-transformers (all-MiniLM-L6-v2)

• 100% Free: No API keys, no subscriptions, no paid APIs

• MCP Standard: Works with Claude, Cursor, OpenCode, and other AI clients

• Parallel Crawling: asyncio-based concurrent data collection

Related MCP server: evo-scry

Installation

Option 1: Plugin Installation (Recommended)

OpenCode:

{
  "plugin": ["deep-search@git+https://github.com/sukirman1901/DeepSearch.git"]
}

Claude Code: Add to .mcp.json or ~/.claude/config.json:

{
  "mcpServers": {
    "deep-search": {
      "command": "python3",
      "args": ["server.py"],
      "cwd": "/path/to/DeepSearch/mcp"
    }
  }
}

Option 2: Manual Installation

# Clone the repository
git clone https://github.com/sukirman1901/DeepSearch.git
cd DeepSearch

# Create virtual environment
python3.12 -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r mcp/requirements.txt

Available Tools (10)

search — Unified Search (7 modes)

crawl — Crawl & Extract

monitor — Persistent Monitoring

webset — Entity Collection

info — Engine Information

research — Deep Research Sessions

Other Tools

Architecture

DeepSearch/
├── mcp/                    # MCP server implementation
│   ├── crawlers/           # 7 specialized crawlers + subpage discovery
│   ├── db/                # ChromaDB + sentence-transformers
│   ├── search/            # Engine, answer, context, streaming, research, monitors, websets, sitemap, extract
│   ├── tests/             # 192 tests
│   ├── server.py          # 10 consolidated MCP tools
│   └── requirements.txt
├── skills/                # AI skills
│   └── using-deep-search/SKILL.md
├── hooks/                 # Session hooks
├── docs/superpowers/specs/ # Design specs
└── README.md

How It Works

1. Crawlers gather raw data from 7 sources (parallel async)

2. Sentence-transformers embeds text to 384-dim vectors

3. ChromaDB stores vectors in memory

4. Search engine performs semantic search

5. AI agent validates and summarizes results

AI Validates Results

Crawlers collect raw data. AI agent downstream validates, scores, and summarizes. Don't just trust crawler output.

Supported Platforms

• OpenCode - Plugin installation via plugin config

• Claude Code - MCP server configuration

• Cursor - Plugin installation

• Codex - Plugin installation

• Kimi Code - Plugin installation

• Gemini CLI - Extension support

• Any MCP-compatible client

License

MIT