Deep Research

@Pink Pixel

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7 months ago

# information-retrieval

A Model Context Protocol (MCP) compliant server designed for comprehensive web research. It uses Tavily's Search and Crawl APIs to gather detailed information on a given topic, then structures this data in a format perfect for LLMs to create high-quality markdown documents.

Overview Tools Comments

Overview

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

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Server Config

{
  "mcpServers": {
    "deep-research": {
      "command": "npx",
      "args": [
        "-y",
        "@pinkpixel/deep-research-mcp"
      ],
      "env": {
        "TAVILY_API_KEY": "tvly-YOUR_ACTUAL_API_KEY_HERE",
        "DOCUMENTATION_PROMPT": "Your custom, detailed instructions for the LLM on how to generate markdown documents from the research data...",
        "RESEARCH_OUTPUT_PATH": "/path/to/your/research/output/folder"
      }
    }
  }
}

Build with ShipAny.