maru-deep-pro-search 0.11.2

Universal AI search MCP server — Perplexity-level quality with zero API keys. Multi-engine web scraping, intelligent ranking, and citation-native answers.

maru-deep-pro-search

Force your AI agent to research before it codes.
Zero API keys · 9-engine failover · BM25+semantic ranking · Native citations

🇰🇷 한국어

🌐 Website · 📦 PyPI · 💻 GitHub

---

What it is

maru-deep-pro-search is an MCP server that gives your AI coding agent live web search superpowers — and forces it to use them before writing any code.

	Built-in Agent Search	maru-deep-pro-search
Engines	1–2, no fallback	9-engine auto-failover
Ranking	Raw engine order	BM25 + semantic + authority/freshness/code-density
Citations	Hallucinated or none	Native [1], [2] IDs with real URLs
Defense	None	72-signature prompt injection + zero-width char sanitization
Enforcement	"Please search first" (ignored)	3-layer technical gatekeeping
Cost	Varies	$0 forever — zero API keys

---

Install

macOS / Linux — recommended (auto-installs uv if needed):

curl -sSL https://raw.githubusercontent.com/claudianus/maru-deep-pro-search/main/scripts/install.sh | bash

Windows (PowerShell):

irm https://raw.githubusercontent.com/claudianus/maru-deep-pro-search/main/scripts/install.ps1 | iex

Manual (pip):

pip install maru-deep-pro-search[semantic] && maru-deep-pro-search setup

The setup wizard auto-detects your AI agent, backs up existing configs, injects MCP settings, and enforces research-first rules.

---

Quick Start

from maru_deep_pro_search.tools import deep_research

result = deep_research(
    "What are the security implications of using pickle in Python?",
    max_sources=5
)
print(result)  # ranked URLs with metadata — agent decides which to fetch

MCP tool decision tree:

• Quick answer? → answer
• Need ranked sources? → web_search
• Deep dive? → deep_research
• Blocked by bot protection? → stealthy_fetch

See AGENTS.md for per-agent setup details.

---

Architecture

MCP Client (Claude, Cursor, Kimi, Windsurf, ...)
        │ JSON-RPC 2.0 / stdio
        ▼
┌──────────────────────────────────────┐
│  maru-deep-pro-search MCP Server     │
│  ├─ 8 Tools (search, fetch, cite)   │
│  ├─ 9-Engine Failover Registry      │
│  ├─ Hybrid Ranking (BM25+semantic)  │
│  ├─ 3-Layer Enforcement             │
│  └─ SQLite KnowledgeStore           │
└──────────────────────────────────────┘

The server contains zero generative LLMs. Your agent's LLM handles all reasoning and synthesis. The server focuses on search quality: multi-engine coverage, intelligent ranking, and clean content extraction.

For deep technical details, see docs/engine_insights.md and docs/lessons_learned.md.

---

8 Tools

Tool	Purpose
answer	Quick answer with inline citations
web_search	Scrape + rank + return cited results
search_with_citations	Pre-numbered sources for academic writing
fetch_page	Extract clean content from a single URL
fetch_bulk	Parallel fetch with deduplication
deep_research	Deep multi-engine search with ranked URLs + metadata
stealthy_fetch	Anti-bot bypass for protected sites
parallel_search	Run multiple searches simultaneously

---

Security

Fetched content is sanitized through a 72-pattern defense layer before reaching your LLM:

• Zero-width character removal (\u200b, \u200c, \u200d)
• Chat-token neutralization (Human:, Assistant: → [REDACTED])
• MCP-specific attack detection (tool poisoning, rug pulls, shadowing)
• Optional semantic similarity anomaly detection

Every tool call is logged to .maru/audit.db with anomaly detection (rapid-fire, oversized results, suspicious params).

See SECURITY.md for disclosure policy.

---

Configuration

All optional. Loaded via pydantic-settings with prefix MARU_SEARCH_.

Variable	Default	Description
ENGINE	duckduckgo_lite	Default search engine
MAX_RESULTS	10	Results per query per engine
MAX_CONCURRENT	5	Parallel fetch limit
MAX_CONCURRENT	5	Parallel fetch limit
TIMEOUT	30.0	Fetch timeout (seconds)
TIMEOUT	30.0	Fetch timeout (seconds)
RETRIES	3	Retry attempts

GitHub Actions Bot Identity (optional)

By default, workflow comments appear as github-actions[bot]. To use a custom bot name and icon:

1. Create a GitHub App at Settings → Developer settings → GitHub Apps → New GitHub App
   • Name: marubot (or your preference)
   • Upload a custom icon (PNG, 200×200)
   • Permissions: Issues (write), Pull requests (write), Actions (read)
2. Install the app on your repository
3. Go to Settings → Secrets and variables → Actions and add:
   • MARUBOT_APP_ID — your GitHub App ID
   • MARUBOT_PRIVATE_KEY — the PEM private key from the app
4. The generated workflow automatically falls back to github-actions[bot] if MARUBOT_TOKEN is not set.

---

CLI Commands

# MCP server (stdio transport)
maru-deep-pro-search

# Setup AI agents with MCP config
maru-deep-pro-search setup
maru-deep-pro-search setup --list
maru-deep-pro-search setup --restore

# Initialize project harness
maru-deep-pro-search init --agents cursor claude

# Generate GitHub Actions workflow
maru-deep-pro-search workflow --force

# Manage plugins
maru-deep-pro-search-plugin list
maru-deep-pro-search-plugin install <git-url>

# Headless deep research (CI/CD friendly)
python -m maru_deep_pro_search.server research "FastAPI vs Django 2025" \
  --output report.md --max-sources 8

---

Docker

# Build
docker build -t maru-search .

# Run with stdio transport
docker run --rm -i maru-search

# With persistent knowledge store
docker run --rm -i -v $(pwd)/.maru:/app/.maru maru-search

---

Troubleshooting

No results from search engine

MARU_SEARCH_ENGINE=bing maru-deep-pro-search

Agent not detected by setup wizard

maru-deep-pro-search setup --agent cursor
maru-deep-pro-search setup --list-agents

High memory usage

# Use lighter search mode
MARU_SEARCH_MAX_RESULTS=5 maru-deep-pro-search

---

Contributing

PRs welcome. See CONTRIBUTING.md for development setup, adding engines, and agent adapters.

---

License

MIT — see LICENSE.