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Deep Research MCP Server

@ssdeanx

代码仓库

基本信息

分类

数据与分析

传输方式

stdio

作者

ssdeanx

关于 Deep Research MCP Server

MCP Deep Research Server using Gemini creating a Research AI Agent

#information-retrieval#research#knowledge#mcp#postgresql#gemini#postgresql-database#knowledge-base#autonomous-agents#ai-powered#research-automation#gemini-ai#mcp-servers#mcp-server#mcp-client#mcp-directory#deep-research#gemini-research#deep-research-google#research-automation-information-retrieval

Deep Research MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

Deep Research MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

常见问题

1

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).

2

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.

3

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.

4

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.

5

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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