Deep Research MCP Server 🚀

Name: Deep Research MCP Server 🚀
Author: ssdeanx

@ssdeanx

リポジトリ

Deep Research MCP Server 🚀 について

MCP Deep Research Server using Gemini creating a Research AI Agent

基本情報

カテゴリ

データと分析

トランスポート

stdio

公開者

ssdeanx

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "deep-research-mcp-ssdeanx": {
      "command": "node",
      "args": [
        "src/db.ts"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Deep Research MCP Server?

Deep Research MCP Server is an AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and Gemini large language models. It is available as a Model Context Protocol (MCP) tool for seamless integration with AI agents.

How to use Deep Research MCP Server?

Install Node.js v22.x, set up environment variables (GEMINI_API_KEY, FIRECRAWL_KEY, DATABASE_URL for PostgreSQL), then build with npm run build. To use as an MCP tool, start the server with node --env-file .env.local dist/mcp-server.js. Alternatively, use the CLI directly with npm run start "your research query".

Key features of Deep Research MCP Server

MCP Integration for use with AI agents
Iterative research with automatic deepening
Intelligent query generation using Gemini LLMs
Configurable depth (1–5) and breadth (1–5)
Produces detailed markdown reports with sources
Concurrent search and result processing
Persistent knowledge storage via PostgreSQL

Use cases of Deep Research MCP Server

Investigate the latest developments in quantum computing across multiple rounds
Build a persistent knowledge base that improves over research sessions
Automate deep-dive literature reviews for technical or academic topics
Generate comprehensive, cited reports for business intelligence

FAQ from Deep Research MCP Server

What is Deep Research MCP Server and how does it differ from a simple search?

Unlike a single web search, Deep Research MCP Server iteratively refines its research direction: it generates follow‑up search queries based on previous learnings and can dive deeper (up to 5 levels) while keeping a broad scope (up to 5 branches per level).

What are the runtime requirements?

It requires Node.js v22+, a Firecrawl API key for web search and content extraction, a Gemini API key for LLM processing, and a PostgreSQL database (local or remote) for persistent storage.

How does the server store and manage research data?

All research findings and visited URLs are stored in a PostgreSQL database. Previously processed URLs are tracked to avoid duplicates, and the agent can reference past learnings to guide new research directions.

What parameters control the research depth and breadth?

The MCP tool accepts query (string), depth (1–5, how many levels of recursion), breadth (1–5, how many parallel directions per level), and an optional existingLearnings array to build upon prior results.

How can I start the MCP server?

After building the project, run node --env-file .env.local dist/mcp-server.js. The server listens for MCP‑compatible client requests. You can also test it with npx @modelcontextprotocol/inspector node ….

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