MCPSearch

MCPSearch

AI-powered multi-source research and crawling platform with MCP integration

  

Overview

MCPSearch is a self-hosted research stack for agents and developers. It combines:

• parallel web search across multiple engines
• HTTP + browser + stealth crawling
• social and developer-source collection
• structured content extraction
• MCP-native tool exposure
• higher-level research workflows via investigate, compare, and trending

The project has grown beyond a simple crawler. The current shape is:

• 29 MCP tools in mcp_server/server.py
• a unified mcpsearch / mcpsearch_multi interface
• shared action routing in mcp_server/handlers.py
• a flagship orchestration layer in agents/research_agent.py

Current Capabilities

• Web search: DuckDuckGo, Google, and Bing aggregation
• Crawling modes:

fast via HTTP only, hybrid via HTTP + Playwright, stealth via anti-bot fallback

• Extraction:

markdown/text extraction, tables, code blocks, images, metadata, JSON-LD/OpenGraph/Microdata via extruct

• Fast parsing:

selectolax on hot search parsing paths with BeautifulSoup fallback

• Social sources:

Reddit, Twitter/X, YouTube, GitHub

• HTTP caching:

shared async client factory with optional Hishel-backed caching on request-heavy paths

• Research workflows:

research_agent, investigate, compare, trending

• Tool discovery:

list_tools, describe_tools, get_crawl_stats

Install

Basic install

git clone https://github.com/JonusNattapong/MCPSearch.git
cd MCPSearch
pip install -e .
playwright install chromium

Development install

make dev

or:

pip install -e ".[dev]"
playwright install chromium

Optional stealth dependency

crawler/stealth.py can use Camoufox when it is installed. If Camoufox is not available, MCPSearch falls back to Playwright-based stealth behavior.

Environment variables

• OPENAI_API_KEY

Optional. Used by summarization flows when AI summaries are enabled.

Quick Start

CLI

# Search
mcpsearch search -q "AI agents"

# Crawl a page
mcpsearch crawl -u "https://example.com"

# Read a page in terminal-friendly format
mcpsearch read -u "https://example.com"

# Research workflow
mcpsearch research --query "browser fingerprinting" --depth deep --summarize

# Compare topics
mcpsearch compare --compare "React" "Vue" "Svelte" --depth medium

# Trending view
mcpsearch trending --max-results 10

# Run MCP server
mcpsearch server

Python / MCP-facing examples

# Unified tool
mcpsearch(action="search", query="LLM agents", limit=5)
mcpsearch(action="crawl", url="https://example.com", mode="hybrid")
mcpsearch(action="reddit", query="python", subreddit="learnpython")
mcpsearch(action="github", query="browser automation", sort="stars")

# Multi-action orchestration
mcpsearch_multi(actions='[
  {"action":"search","query":"agent memory patterns"},
  {"action":"reddit","query":"LocalLLaMA"},
  {"action":"github","query":"llm agents","sort":"stars"}
]')

# Flagship research tools
investigate(topic="Python async scraping", depth="deep", include_social=True)
compare(topics="React,Vue,Svelte", depth="medium", max_sources=3)
trending(platforms="reddit,github", limit=10)

MCP Integration

Claude Desktop

{
  "mcpServers": {
    "mcpsearch": {
      "command": "python",
      "args": ["-m", "mcp_server"],
      "cwd": "/path/to/MCPSearch",
      "env": {
        "OPENAI_API_KEY": ""
      }
    }
  }
}

Cursor

{
  "mcpServers": {
    "mcpsearch": {
      "command": "python",
      "args": ["-m", "mcp_server"],
      "cwd": "/path/to/MCPSearch"
    }
  }
}

Custom MCP client

{
  "command": "python",
  "args": ["-m", "mcp_server"],
  "transport": "stdio"
}

Tool Map

Unified tools

• mcpsearch
• mcpsearch_multi

Search and crawl tools

• web_search
• search_and_summarize
• smart_search
• deep_search
• crawl_url
• hybrid_crawl
• crawl_recursive
• extract_content
• get_crawl_stats

Social tools

• search_reddit
• get_subreddit
• get_reddit_post
• search_twitter
• get_user_tweets
• search_youtube
• get_youtube_channel
• get_youtube_content
• search_github
• get_github_user
• get_github_repo
• get_github_readme

Research tools

• research_agent
• investigate
• compare
• trending

Discovery tools

• list_tools
• describe_tools

Recommended Entry Points

If you are integrating MCPSearch into an agent:

• start with list_tools and describe_tools
• prefer mcpsearch for simple routing
• use mcpsearch_multi when you want parallel source gathering
• use investigate for richer topic-oriented research
• use compare when the output should be side-by-side
• use trending for source discovery and early signal collection

Research Workflows

investigate

Best when you want one topic explored across search, crawl, and social sources.

investigate(
    topic="anti-bot browser strategies",
    depth="deep",
    include_social=True,
    include_summary=True,
    max_sources=5,
)

compare

Best when you want repeated shallow or medium investigations and a compact comparison result.

compare(
    topics="Playwright,Selenium,Camoufox",
    depth="medium",
    max_sources=3,
)

trending

Best when you want new leads before deeper crawling.

trending(
    platforms="reddit,github",
    limit=10,
)

Architecture

Request flow

Query / URL / Topic
        |
        v
  mcpsearch / direct tool
        |
        v
 mcp_server/handlers.py
        |
        +--> search/aggregator.py
        +--> crawler/engine.py
        +--> crawler/hybrid.py
        +--> crawler/stealth.py
        +--> social/*.py
        +--> agents/research_agent.py

Crawl strategy

fast    -> HTTP only
hybrid  -> HTTP first, then browser rendering when needed
stealth -> multi-browser / anti-bot fallback path

Current project structure

MCPSearch/
├── agents/                 # Higher-level research orchestration
├── crawler/                # HTTP, hybrid, stealth, extraction logic
├── mcp_server/             # MCP server, unified tools, shared handlers
├── search/                 # Search aggregation
├── social/                 # Reddit, Twitter/X, YouTube, GitHub scrapers
├── summarizer/             # AI summarization helpers
├── tests/                  # Workflow and unit tests
├── utils/                  # Cache, dedup, rate limiting
├── cli.py                  # CLI entry point
├── Makefile                # Dev/test/release commands
└── pyproject.toml          # Package metadata and dependencies

Development

Useful commands

make install
make dev
make test
make test-cov
make lint
make lint-fix
make format
make server
python3 scripts/benchmark_search_and_crawl.py

Focused test commands

make test-hybrid
make test-rate-limiter
pytest tests/test_extractor.py -v
pytest tests/test_search_parsers.py -v
pytest tests/test_mcp_integration.py -v
pytest tests/test_mcp_tools.py -v

Release

make patch
make minor
make major

Version is sourced from mcpspider/version.py.

Project Status Notes

• The README now reflects mcpsearch / mcpsearch_multi, not the older scout naming.
• Playwright is part of declared dependencies.
• Camoufox support exists in code, but is optional at install time.
• The main research direction is now orchestration, attribution, and multi-source analysis, not just single-page crawling.

Practical Next Improvements

See docs/USEFUL_LIBS.md for a curated list of libraries and implementation tricks that fit the current architecture.

Legal and Ethical Usage

Use MCPSearch responsibly.

• Respect target site policies and applicable law.
• Use rate limiting and caching to reduce load.
• Review platform terms before large-scale scraping.
• Avoid collecting or redistributing restricted personal data.

Contributing

Contribution guidance lives in CONTRIBUTING.md.

License

MIT. See LICENSE.