Which integrations are available for this server?

Supports containerized deployment of the Presearch MCP server with Docker and Docker Compose configurations for easy orchestration. Uses environment variable configuration for managing API keys, cache settings, and server configuration. Includes ESLint for code quality and linting during development. Version control system used for managing the project repository. Hosts the project repository, issue tracking, and community discussions. Supports Markdown as an export format for search results and scraped content. Built on Node.js runtime (requires v20.0.0 or higher) for server execution. Uses npm for package management and dependency installation. Includes Prettier for code formatting during development. Provides Redis-compatible intelligent caching with configurable TTL and key limits for improved performance. Uses Shields.io badges in documentation for displaying project status and metadata.

How do I use Presearch MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Presearch MCP Server find recent news about quantum computing breakthroughs" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Presearch MCP Server by NosytLabs

🌟 Overview

The Presearch MCP Server is a professional-grade integration bridge that connects AI assistants (like Claude, Cursor, and Trae) to the Presearch decentralized search engine.

Unlike traditional search APIs that track user behavior, Presearch offers a decentralized, privacy-centric alternative. This server enables your AI to:

🛡️ Search Anonymously	🔍 Scrape Intelligently	🧠 Research Deeply	📊 Monitor Nodes
No IP tracking or search history logging	Extract clean content from modern, dynamic websites	Perform multi-step investigations autonomously	Track the status and earnings of Presearch nodes

🔥 Real-World Examples

See what's possible when you give your AI access to Presearch.

1. 🧠 Deep Research Mode

User Prompt: "Research the effects of climate change on coastal real estate markets."

Tool Used: presearch_deep_research

{ "query": "climate change effects on coastal real estate", "breadth": 5, "depth": 2, "research_focus": "market" }

Result: The agent autonomously performs a multi-step investigation:

Initial Search: Queries Presearch for broad market trends.
Analysis: Identifies key sub-topics (insurance rates, flood risk zones, property devaluation).
Deep Dive: Executes targeted sub-searches for each topic.
Synthesis: Returns a comprehensive report citing 14+ distinct sources, including academic papers and market reports, with no tracking of the search intent.

2. ⚡ Search & Scrape (Fast Context)

User Prompt: "Get me the latest specs for the iPhone 16 Pro and summarize the camera upgrades."

Tool Used: presearch_search_and_scrape

{ "query": "iPhone 16 Pro camera specs official", "limit": 3 }

Result:

Search: Finds the top 3 most relevant pages (Apple Official, TechCrunch, The Verge).
Scrape: Immediately fetches the full HTML content of all 3 pages in parallel using a headless browser.
Output: Returns 35kb of clean text content.
AI Action: The AI reads the raw specs and generates a perfect summary of the 48MP Ultra Wide camera and 5x Telephoto lens features.

3. 🛡️ Privacy-First Market Analysis

User Prompt: "Find competitors to our new SaaS product 'StealthMode' without alerting them via analytics."

Tool Used: presearch_ai_search

{ "query": "StealthMode SaaS competitors", "safesearch": "strict", "freshness": "month" }

Result:

Anonymity: The searches are routed through Presearch's decentralized node network. The competitor websites see traffic from generic Presearch nodes, not your corporate IP address.
Outcome: A list of 10 direct competitors launched in the last month, gathered without leaving a digital footprint.

🛡️ What is Presearch?

Presearch is a decentralized search engine built on blockchain technology that rewards community members with Presearch tokens (PRE) for their usage, contribution to, and promotion of the platform.

Why Presearch Matters for AI

🚫 Uncensored Access	🔒 Privacy Protection	🌐 Community Infrastructure
Results are not filtered by central authorities	Your AI's queries are not profiled by ad-tech giants	Search index powered by independent community nodes
Complete view of the web	Proprietary data remains private	Resilient, distributed control

💡 Key Features

🛡️ Privacy & Security

Decentralized Infrastructure: Leverages Presearch's distributed node network
Bearer Token Auth: Secure, standard authentication for API access
No Data Persistence: The server is stateless; no user queries are stored on disk

🔧 Robust Tooling

Deep Research Mode: Recursive search and analysis capabilities
Smart Scraping: Headless browser integration to scrape dynamic JS-heavy websites
Flexible Input Handling: Tools accept JSON strings and loose types for maximum compatibility with LLMs
Multi-Format Export: Export results to JSON, CSV, Markdown, HTML, or PDF

🚀 Enterprise Ready

Intelligent Caching: Configurable TTL and memory limits
Rate Limiting & Retries: Robust error handling with exponential backoff
Health Monitoring: Real-time status checks for API connectivity

🛠️ Available Tools

Tool Name	Description	Key Parameters
`presearch_ai_search`	Standard web search optimized for AI	`query`, `count`, `safesearch`, `freshness`, `content_categories`
`presearch_deep_research`	Autonomous multi-step research agent	`query`, `depth`, `breadth`, `focus`, `location`
`presearch_search_and_scrape`	Search and immediately scrape top results	`query`, `scrape_count`, `include_text`, `location`
`scrape_url_content`	Scrape content from specific URLs	`urls`, `include_text`, `timeout_ms`
`analyze_content`	Analyze content quality and relevance	`content`, `include_quality_assessment`, `custom_keywords`
`export_search_results`	Export search results to files	`count`, `format` (json/csv/md/html/pdf), `file_output`
`presearch_site_export`	Advanced export with scraping and analysis	`query`, `format`, `file_output`, `include_analysis`, `scrape_content`
`presearch_node_status`	Monitor Presearch node health	`node_api_key`, `stats`, `connected`, `include_inactive`
`cache_stats`	View internal cache metrics	(None)
`cache_clear`	Clear the internal cache	(None)
`presearch_health_check`	Verify API connectivity	(None)

Note: All tools support robust input parsing. Parameters can be passed as native types (numbers, booleans, arrays) or as strings/JSON strings (e.g., "true", "10", "['url1', 'url2']").

📝 Available Prompts

The server provides built-in prompts to help you get started with common tasks:

Prompt Name	Purpose
`presearch-deep-dive`	Conduct deep research on a specific topic
`presearch-news`	Find the latest news about a topic from the last 24 hours
`presearch-fact-check`	Verify a claim or statement with evidence
`presearch-market-analysis`	Analyze a market sector or product category
`presearch-node-monitor`	Check the status and earnings of your Presearch nodes
`presearch-product-review`	Research reviews and sentiment for a product
`presearch-academic`	Conduct academic research prioritizing .edu and journals
`presearch-tutorial`	Learn how to use a specific tool effectively

📚 Resources

The server exposes the following resources for configuration and debugging:

Resource URI	Description
`presearch://config`	View current server configuration (secrets masked)
`presearch://rate-limits`	Check current API rate limit status
`presearch://supported-countries`	List of supported ISO 3166-1 alpha-2 country codes
`presearch://supported-languages`	List of supported BCP 47 language codes

⚙️ Configuration

The server can be configured via environment variables or MCP settings.

Environment Variables

Variable	Description	Default
`PRESEARCH_API_KEY`	Your Presearch API Key (Required)	-
`PRESEARCH_BASE_URL`	API Endpoint URL	`https://na-us-1.presearch.com`
`PRESEARCH_TIMEOUT`	Request timeout in ms	`10000`
`LOG_LEVEL`	Logging verbosity (`info`, `debug`, `error`)	`info`

JSON Configuration Schema

When using Smithery or an MCP client, the configuration object supports:

{ "apiKey": "YOUR_KEY", "rateLimit": { "maxRequests": 100, "windowMs": 60000 }, "cache": { "enabled": true, "ttl": 300 }, "search": { "defaultSafeSearch": "moderate", "defaultLanguage": "en-US" } }

🚀 Quick Start

1. Get an API Key

Sign up at Presearch.io to obtain your API key.

2. Run with npx

npx presearch-mcp-server

3. Deploy via Smithery

Use the button above or run:

npx -y @smithery/cli@latest install @NosytLabs/presearch-search-api-mcp --client claude

🧪 Development

Install Dependencies

npm install

Run Tests

# Run real API tests (requires .env with API key) npm run test:search # Run mock tool tests npm run test:tools

Build & Lint

npm run lint npm run format

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Step	Action
1	Fork the repository
2	Create your feature branch (`git checkout -b feature/AmazingFeature`)
3	Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4	Push to the branch (`git push origin feature/AmazingFeature`)
5	Open a Pull Request

📜 License

This server cannot be installed

-

security - not tested

A

license - permissive license

-

quality - not tested

How are these scores calculated?

Resources

GitHub Repository

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