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Deep Research MCP Server

@ssdeanx

RepositoryHomepage

Basic information

Category

Data & Analytics

License

MIT

Transports

stdio

Author

ssdeanx

About 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 config

Add this server to your MCP-compatible client using the configuration below.

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

Deep Research MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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

Frequently asked questions

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.

Comments

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