Research Powerpack MCP

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⚡ Get Started • ✨ Key Features • 🎮 Usage & Examples • ⚙️ API Key Setup • 🆚 Why This Slaps

research-powerpack-mcp is the research assistant your AI wishes it had. Stop asking your LLM to guess about things it doesn't know. This MCP server acts like a senior researcher, searching the web, mining Reddit discussions, scraping documentation, and synthesizing everything into perfectly structured context so your AI can actually give you answers worth a damn.

How it slaps:

• You: "What's the best database for my use case?"

• AI + Powerpack: Searches Google, mines Reddit threads, scrapes docs, synthesizes findings.

• You: Get an actually informed answer with real community opinions and citations.

• Result: Ship better decisions. Skip the 47 browser tabs.

💥 Why This Slaps Other Methods

Manually researching is a vibe-killer. research-powerpack-mcp makes other methods look ancient.

We're not just fetching random pages. We're building high-signal, low-noise context with CTR-weighted ranking, smart comment allocation, and intelligent token distribution that prevents massive responses from breaking your LLM's context window.

🚀 Get Started in 60 Seconds

1. Install

2. Configure Your MCP Client

Claude Desktop

Add to your claude_desktop_config.json:

or quick install (for MacOS):

Claude Code (CLI)

One command to rule them all:

Or manually add to ~/.claude.json:

Cursor/Windsurf

Add to .cursor/mcp.json or equivalent:

✨ Zero Crash Promise: Missing API keys? No problem. The server always starts. Tools just return helpful setup instructions instead of exploding.

✨ Feature Breakdown: The Secret Sauce

🎮 Tool Reference

web_search

Batch web search using Google via Serper API. Search up to 100 keywords in parallel.

Supports Google operators: site:, -exclusion, "exact phrase", filetype:

search_reddit

Search Reddit via Google with automatic site:reddit.com filtering.

Search operators: intitle:keyword, "exact phrase", OR, -exclude

get_reddit_post

Fetch Reddit posts with smart comment allocation (1,000 comment budget distributed automatically).

Smart Allocation:

• 2 posts → ~500 comments/post (deep dive)

• 10 posts → ~100 comments/post

• 50 posts → ~20 comments/post (quick scan)

scrape_links

Universal URL content extraction with automatic fallback modes.

Automatic Fallback: Basic → JS rendering → JS + US geo-targeting

deep_research

AI-powered batch research with web search and citations.

Token Allocation: 32,000 tokens distributed across questions:

• 2 questions → 16,000 tokens/question (deep dive)

• 10 questions → 3,200 tokens/question (rapid multi-topic)

⚙️ Environment Variables & Tool Availability

Research Powerpack uses a modular architecture. Tools are automatically enabled based on which API keys you provide:

Configuration Examples

🔑 API Key Setup Guides

What you get

• Fast Google search results via API

• Enables web_search and search_reddit tools

Setup Steps

1. Go to serper.dev

2. Click "Get API Key" (top right)

3. Sign up with email or Google

4. Copy your API key from the dashboard

5. Add to your config:

   SERPER_API_KEY=your_key_here

Pricing

• Free: 2,500 queries/month

• Paid: $50/month for 50,000 queries

What you get

• Full Reddit API access

• Fetch posts and comments with upvote sorting

• Enables get_reddit_post tool

Setup Steps

1. Go to reddit.com/prefs/apps

2. Scroll down and click "create another app..."

3. Fill in:

   • Name: research-powerpack (or any name)

   • App type: Select "script" (important!)

   • Redirect URI: http://localhost:8080

4. Click "create app"

5. Copy your credentials:

   • Client ID: The string under your app name

   • Client Secret: The "secret" field

6. Add to your config:

   REDDIT_CLIENT_ID=your_client_id REDDIT_CLIENT_SECRET=your_client_secret

What you get

• JavaScript rendering support

• Geo-targeting and CAPTCHA handling

• Enables scrape_links tool

Setup Steps

1. Go to scrape.do

2. Click "Start Free"

3. Sign up with email

4. Copy your API key from the dashboard

5. Add to your config:

   SCRAPEDO_API_KEY=your_key_here

Credit Usage

• Basic scrape: 1 credit

• JavaScript rendering: 5 credits

• Geo-targeting: +25 credits

What you get

• Access to 100+ AI models via one API

• Enables deep_research tool

• Enables AI extraction in scrape_links

Setup Steps

1. Go to openrouter.ai

2. Sign up with Google/GitHub/email

3. Go to openrouter.ai/keys

4. Click "Create Key"

5. Copy the key (starts with sk-or-...)

6. Add to your config:

   OPENROUTER_API_KEY=sk-or-v1-xxxxx

Recommended Models for Deep Research

Recommended Models for AI Extraction (use_llm in scrape_links)

Note: RESEARCH_MODEL and LLM_EXTRACTION_MODEL are independent. You can use a powerful model for deep research and a faster/cheaper model for content extraction, or vice versa.

🔥 Recommended Workflows

Research a Technology Decision

Competitive Analysis

Debug an Obscure Error

🔥 Enable Full Power Mode

For the best research experience, configure all four API keys:

This unlocks:

• 5 research tools working together

• AI-powered content extraction in scrape_links

• Deep research with web search and citations

• Complete Reddit mining (search → fetch → analyze)

Total setup time: ~10 minutes. Total free tier value: ~$50/month equivalent.

🛠️ Development

🏗️ Architecture (v3.4.0+)

The codebase uses a YAML-driven configuration system with aggressive LLM optimization (v3.5.0+):

Core Architecture

Adding a new tool:

1. Add tool definition to tools.yaml

2. Create handler in src/tools/

3. Register in src/tools/registry.ts

See docs/refactoring/04-migration-guide.md for detailed instructions.

Performance & Stability (v3.5.1+)

All parallel operations use bounded concurrency to prevent CPU spikes and API rate limits:

Additional optimizations:

• YAML config cached in memory (no repeated disk reads)

• Async file I/O (no event loop blocking)

• Pre-compiled regex patterns for hot paths

• Reddit auth token deduplication (prevents concurrent token requests)

LLM Optimization (v3.5.0+)

All tools include aggressive guidance to force LLMs to use them optimally:

Key Enhancements:

• search_reddit: Minimum 10 queries (was 3), 10-category formula

• deep_research: 7-section question template, file attachment requirements

• scrape_links: Extraction template with OR statements, use_llm=true push

• web_search: Minimum 3 keywords, search operator examples

• file_attachments: Numbered 5-section description template

See docs/refactoring/07-llm-optimization-summary.md for full details.

🔥 Common Issues & Quick Fixes

Built with 🔥 because manually researching for your AI is a soul-crushing waste of time.

MIT © Yiğit Konur