Skip to content

OVERVIEW.md

Latest commit

 

History

History
111 lines (81 loc) · 4.3 KB

OVERVIEW.md

File metadata and controls

111 lines (81 loc) · 4.3 KB

Deep Research MCP Server ✨

Dream it, Pixel it — by Pink Pixel

🚀 Project Purpose

The Deep Research MCP Server is a Model Context Protocol (MCP) compliant server for comprehensive, up-to-date web research. It leverages Tavily's Search & Crawl APIs to gather, aggregate, and structure information for LLM-powered documentation generation.

🧩 Architecture Overview

  • MCP Server (Node.js, TypeScript)
  • Stdio Transport for agent/server communication
  • Tavily API Integration (Search + Crawl)
  • Configurable Documentation Prompt (default, ENV, or per-request)
  • Structured JSON Output for LLMs
Architecture Diagram (Text) 
[LLM/Agent]
    │
    ▼
[Deep Research MCP Server]
    │   ├─> Tavily Search API
    │   └─> Tavily Crawl API
    ▼
[Aggregated JSON Output + Documentation Instructions]

✨ Main Features

  • Multi-Step Research: Combines AI-powered search with deep content crawling
  • Structured Output: JSON with query, search summary, findings, and doc instructions
  • Configurable Prompts: Override documentation style via ENV or per-request
  • Configurable Output Path: Specify where research documents and images should be saved
  • Granular Control: Fine-tune search/crawl with many parameters
  • MCP Compliant: Plug-and-play for agent ecosystems
  • Resource Optimized: Memory tracking, auto-garbage collection, and hardware acceleration support

🛠️ Key Dependencies

  • @modelcontextprotocol/sdk (v1.11.1) — MCP server framework
  • @tavily/core (v0.5.2) — Tavily Search & Crawl APIs
  • dotenv (v16.5.0) — Environment variable management

📁 File Structure

deep-research-mcp/
├── dist/                # Compiled JS output
├── src/
│   └── index.ts         # Main server logic
├── assets/              # Project assets (logo)
├── README.md            # Full documentation
├── OVERVIEW.md          # (You are here!)
├── example_config.json  # Example MCP config
├── package.json         # Project metadata & dependencies
├── tsconfig.json        # TypeScript config
├── CHANGELOG.md         # Version history and changes

⚡ Usage & Integration

  • Install & Run:
    • npx @pinkpixel/deep-research-mcp (quickest)
    • Or clone & npm install, then npm start
  • Configure: Set TAVILY_API_KEY in your environment (see README.md)
  • Integrate: Connect to your LLM/agent via MCP stdio
  • Customize: Override documentation prompt via ENV or tool argument
  • Output: Specify where research documents and images should be saved
  • Performance: Enable hardware acceleration with hardware_acceleration: true parameter

🔄 Recent Updates

  • Optimized Resource Usage: Reduced default crawl limits to prevent excessive memory consumption
  • MCP Protocol Compliance: Fixed response structure to properly follow MCP specifications
  • Improved Error Handling: Better error reporting and handling of timeouts
  • Performance Optimizations: Added optional hardware acceleration (WebGPU) support
  • Smarter Crawling: Added domain validation to focus crawling and prevent overly broad searches

See CHANGELOG.md for complete version history

📚 More Info

  • See README.md for full usage, parameters, and troubleshooting
  • Example config: example_config.json
  • License: MIT
  • Node.js: >=18.0.0 required

Made with ❤️ by Pink Pixel