DeepRe - AI駆動の深い調査レポート生成ツール

Name: DeepRe - AI駆動の深い調査レポート生成ツール
Author: hirokidaichi

@hirokidaichi

Repository

About DeepRe - AI駆動の深い調査レポート生成ツール

local mcp server perplexity

Basic information

Config

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

{
  "mcpServers": {
    "deepre": {
      "command": "deno",
      "args": [
        "task",
        "install"
      ]
    }
  }
}

Tools

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Overview

What is DeepRe - AI駆動の深い調査レポート生成ツール?

DeepRe - AI駆動の深い調査レポート生成ツール is a Deno-based CLI tool that automatically generates in-depth research reports on a specified theme. It leverages the Google Gemini AI API to iteratively collect and evaluate information until sufficient quality is reached, then produces a comprehensive report in Markdown format. The tool is designed for users who need systematic, automated research on any topic, with full Japanese language support.

How to use DeepRe - AI駆動の深い調査レポート生成ツール?

Install globally with deno task install, then run deepre "調査テーマ". Optionally specify an API key with -k, output directory with -o (default ./research), Gemini model with -m (default gemini-2.0-flash), and iteration count with -i (default 10). The API key can also be set via the GEMINI_API_KEY environment variable. Development tasks include deno task deepre for running in dev mode and deno task check-all for linting, type checking, formatting, and tests.

Key features of DeepRe - AI駆動の深い調査レポート生成ツール

Iterative research process with automatic quality evaluation
Integrates with Gemini 2.0 AI models for advanced search and analysis
Automatically generates an optimal research plan from a theme
Full Japanese language support for themes and output
Produces formatted Markdown reports

Use cases of DeepRe - AI駆動の深い調査レポート生成ツール

Preliminary research for academic projects
Market trend and competitive analysis
Technology trend investigation
In-depth information gathering on specific topics

FAQ from DeepRe - AI駆動の深い調査レポート生成ツール

What runtime and API key are required?

Deno runtime is required, along with a Google Gemini API key. The key can be passed via the GEMINI_API_KEY environment variable or the -k command-line option.

What is the default model and iteration count?

The default Gemini model is gemini-2.0-flash and the default iteration count is 10. You can override both with the -m and -i flags respectively.