Deep-research

Name: Deep-research
Author: ssdeanx

@ssdeanx

リポジトリホームページ

Deep-research について

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

基本情報

カテゴリ

データと分析

ライセンス

MIT

ランタイム

node

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

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?

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

FAQ from Deep-research

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.

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).

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.

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.

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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