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Deep Research MCP Server

@ssdeanx

代码仓库主页

基本信息

分类

数据与分析

许可证

MIT

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

#research#ai#research-tool#research-paper#research-and-development#ai-agents#ai-research#ai-researcher#airesearch#aiagent#ai-research-assistant#mcp-server

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-server": {
      "command": "node",
      "args": [
        "--env-file",
        ".env.local",
        "dist/mcp-server.js"
      ]
    }
  }
}

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?

Deep-research is an AI-powered research assistant that runs as a Model Context Protocol (MCP) server. It uses Google Gemini 2.5 Flash with Google Search Grounding to perform iterative, deep research on user queries, producing professional Markdown reports—all without requiring web scraping dependencies.

How to use Deep-research?

To run as an MCP tool, start the server with node --env-file .env.local dist/mcp-server.js and invoke the deep-research tool with parameters such as query, depth, and breadth. Alternatively, use the standalone CLI by running npm run start "your research query". The server is configured via environment variables like GEMINI_API_KEY, GEMINI_MODEL, and CONCURRENCY_LIMIT.

Key features of Deep-research

  • MCP server/tool integration for seamless agent workflows
  • Gemini 2.5 Flash pipeline with structured JSON outputs
  • Iterative deep dive with query refinement and context carryover
  • Configurable depth and breadth for exploration scope
  • Semantic and recursive splitting for robust content analysis
  • Batching and LRU caching for concurrency-limited performance
  • Generates professional Markdown reports with abstract, methodology, and references

Use cases of Deep-research

  • Conducting iterative literature reviews on emerging topics
  • Generating structured research reports for multi-agent systems
  • Exploring multi-faceted questions with controlled depth and breadth
  • Integrating as a research tool within MCP-compatible agent clients

常见问题

1

What are the system requirements for Deep-research?

Node.js v22.x and a Google Gemini API key are required. The server runs on the Gemini 2.5 Flash model.

2

How do I configure Deep-research?

Configuration is done through environment variables set in a `.env.local` file. Required: `GEMINI_API_KEY`. Optional: `GEMINI_MODEL`, `GEMINI_MAX_OUTPUT_TOKENS`, `CONCURRENCY_LIMIT`, and flags to enable Gemini tools (Google Search Grounding, Code Execution, Functions).

3

Can I use Deep-research without an MCP client?

Yes, Deep-research can be run as a standalone CLI tool using `npm run start "query"`, bypassing the MCP server.

4

Does Deep-research require web scraping dependencies?

No, the pipeline uses Google Search Grounding via Gemini’s tool use, so no web scraping library is needed.

5

What transport and authentication does the MCP server use?

The MCP server uses the standard MCP transport. The `GEMINI_API_KEY` must be supplied to the server process as an environment variable for authentication.

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