Research Powerpack MCP

🔥 REDDIT SEARCH - MINIMUM 10 QUERIES, RECOMMENDED 20+

This tool is designed for consensus analysis through MULTIPLE diverse queries. Using 1-3 queries = wasting the tool's power. You MUST use 10+ queries minimum.

Budget: 10 results per query, all run in parallel.

• 10 queries = 100 results

• 20 queries = 200 results (RECOMMENDED)

• 50 queries = 500 results (comprehensive)

10-Category Query Formula - Each query targets a DIFFERENT angle. NO OVERLAP!

1. Direct topic: "[topic] [platform]" Example: "YouTube Music Mac app"

2. Recommendations: "best/recommended [topic]" Example: "best YouTube Music client Mac"

3. Specific tools: Project names, GitHub repos Example: "YTMDesktop", "th-ch youtube-music"

4. Comparisons: "[A] vs [B]" Example: "YouTube Music vs Spotify Mac desktop"

5. Alternatives: "[topic] alternative/replacement" Example: "YouTube Music Mac alternative"

6. Subreddits: "r/[subreddit] [topic]" - different communities have different perspectives Example: "r/macapps YouTube Music", "r/opensource YouTube Music"

7. Problems/Issues: "[topic] issues/crashes/problems" Example: "YouTube Music Mac crashes", "YTM desktop performance problems"

8. Year-specific: Add "2024" or "2025" for recent discussions Example: "best YouTube Music Mac 2024"

9. Features: "[topic] [specific feature]" Example: "YouTube Music offline Mac", "YTM lyrics desktop"

10. Developer/GitHub: "[topic] GitHub/open source/electron" Example: "youtube-music electron GitHub", "YTM desktop open source"

Search Operators:

• intitle: - Search in post titles only

• "exact phrase" - Match exact phrase

• OR - Match either term

• -exclude - Exclude term

• All queries auto-add site:reddit.com

Example showing all 10 categories: ❌ BAD: {"queries": ["best YouTube Music app"]} → 1 vague query, misses 90% of consensus ✅ GOOD: {"queries": ["YouTube Music Mac app", "best YTM client Mac", "YTMDesktop Mac", "YouTube Music vs Spotify Mac", "YouTube Music Mac alternative", "r/macapps YouTube Music", "YTM Mac crashes", "YouTube Music Mac 2024", "YTM offline Mac", "youtube-music GitHub", ...expand to 20 queries]} → comprehensive multi-angle coverage

Pro Tips:

1. Use ALL 10 categories - Each reveals different community perspectives

2. Target specific subreddits - Different communities have different expertise

3. Include year numbers - "2024", "2025" filters for recent discussions

4. Add comparison keywords - "vs", "versus" find decision threads

5. Include problem keywords - "issue", "bug", "crash" find real experiences

6. Vary phrasing - "best", "top", "recommended" capture different discussions

7. Use technical terms - "electron", "GitHub", "API" find developer perspectives

8. NO DUPLICATES - Each query must target a unique angle

Workflow: search_reddit → sequentialthinking (evaluate results) → get_reddit_post OR search again → sequentialthinking → synthesize

REMEMBER: More queries = better consensus detection = higher quality results!

🔥 FETCH REDDIT POSTS - 2-50 URLs, RECOMMENDED 10-20+

This tool fetches Reddit posts with smart comment allocation. Using 2-5 posts = missing community consensus. Use 10-20+ for broad perspective.

Comment Budget: 1,000 total comments distributed automatically across posts.

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

• 10 posts: ~100 comments/post (balanced)

• 20 posts: ~50 comments/post (RECOMMENDED: broad)

• 50 posts: ~20 comments/post (max coverage)

Comment allocation is AUTOMATIC - you don't need to calculate!

When to use different post counts:

2-5 posts: Deep dive on specific discussions

• Use when: You found THE perfect thread and want all comments

• Trade-off: Deep but narrow perspective

10-15 posts: Balanced depth + breadth (GOOD)

• Use when: Want good comment depth across multiple discussions

• Trade-off: Good balance of depth and coverage

20-30 posts: Broad community perspective (RECOMMENDED)

• Use when: Want to see consensus across many discussions

• Trade-off: Less comments per post but more diverse opinions

40-50 posts: Maximum coverage

• Use when: Researching controversial topic, need all perspectives

• Trade-off: Fewer comments per post but comprehensive coverage

Example: ❌ BAD: {"urls": ["single_url"]} → 1 perspective, could be biased/outdated ✅ GOOD: {"urls": [20 URLs from diverse subreddits: programming, webdev, node, golang, devops, etc.]} → comprehensive community perspective

Pro Tips:

1. Use 10-20+ posts - More posts = broader community perspective

2. Mix subreddits - Different communities have different expertise and perspectives

3. Include various discussion types - Best practices, comparisons, problems, solutions

4. Let comment allocation auto-adjust - Don't override max_comments unless needed

5. Use after search_reddit - Get URLs from search, then fetch full content here

CRITICAL: Comments often contain the BEST insights, solutions, and real-world experiences. Always set fetch_comments=true unless you only need post titles.

Workflow: search_reddit (find posts) → get_reddit_post (fetch full content + comments)

🔥 DEEP RESEARCH - 2-10 QUESTIONS, RECOMMENDED 5+

This tool runs 2-10 questions IN PARALLEL with AI-powered research. Using 1-2 questions = wasting the parallel research capability!

Token Budget: 32,000 tokens distributed across questions.

• 2 questions: 16,000 tokens each (deep dive)

• 5 questions: 6,400 tokens each (RECOMMENDED: balanced)

• 10 questions: 3,200 tokens each (comprehensive multi-topic)

All questions research in PARALLEL - no time penalty for more questions!

When to use this tool:

• Multi-perspective analysis on related topics

• Researching a domain from multiple angles

• Validating understanding across different aspects

• Comparing approaches/technologies side-by-side

• Deep technical questions requiring comprehensive research

Question Template - Each question MUST include these sections:

1. 🎯 WHAT I NEED: Clearly state what you're trying to achieve or understand

2. 🤔 WHY I'M RESEARCHING: What decision does this inform? What problem are you solving?

3. 📚 WHAT I ALREADY KNOW: Share current understanding so research fills gaps, not repeats basics

4. 🔧 HOW I'LL USE THIS: Practical application - implementation, debugging, architecture

5. ❓ SPECIFIC QUESTIONS (2-5): Break down into specific, pointed sub-questions

6. 🌐 PRIORITY SOURCES: (optional) Preferred docs/sites to prioritize

7. ⚡ FOCUS AREAS: (optional) What matters most - performance, security, etc.

ATTACH FILES when asking about code - THIS IS MANDATORY:

• 🐛 Bugs/errors → Attach the failing code

• ⚡ Performance issues → Attach the slow code paths

• ♻️ Refactoring → Attach current implementation

• 🔍 Code review → Attach code to review

• 🏗️ Architecture → Attach relevant modules

Research without code context for code questions is generic and unhelpful!

Example: ❌ BAD: {"questions": [{"question": "Research React hooks"}]} → 1 vague question, no template, no context, wastes 90% capacity

✅ GOOD:

Pro Tips:

1. Use 5-10 questions - Maximize parallel research capacity

2. Follow the template - Include all 7 sections for each question

3. Be specific - Include version numbers, error codes, library names

4. Add 2-5 sub-questions - Break down what you need to know

5. Attach files for code questions - MANDATORY for bugs/performance/refactoring

6. Describe files thoroughly - Explain what the file is and what to focus on

7. Specify focus areas - "Focus on X, Y, Z" for prioritization

8. Group related questions - Research a domain from multiple angles

Scope Expansion Triggers - Iterate when:

• Results mention concepts you didn't research

• Answers raise new questions you should explore

• You realize initial scope was too narrow

• You discover related topics that matter

Workflow: deep_research (3-5 questions) → sequentialthinking (evaluate, identify gaps) → OPTIONAL: deep_research AGAIN with NEW questions based on learnings → sequentialthinking (synthesize) → final decision

REMEMBER:

• ALWAYS think after getting results (digest and identify gaps!)

• DON'T assume first research is complete (iterate based on findings!)

• USE learnings to ask better questions (results = feedback!)

• EXPAND scope when results reveal new important areas!

🔥 WEB SCRAPING - 1-50 URLs, RECOMMENDED 3-5. ALWAYS use_llm=true

This tool has TWO modes:

1. Basic scraping (use_llm=false) - Gets raw HTML/text - messy, requires manual parsing

2. AI-powered extraction (use_llm=true) - Intelligently extracts what you need ⭐ USE THIS!

⚡ ALWAYS SET use_llm=true FOR INTELLIGENT EXTRACTION ⚡

Why use AI extraction (use_llm=true):

• Filters out navigation, ads, footers automatically

• Extracts ONLY what you specify in what_to_extract

• Handles complex page structures intelligently

• Returns clean, structured content ready to use

• Saves hours of manual HTML parsing

• Cost: pennies (~$0.01 per 10 pages)

Token Budget: 32,000 tokens distributed across URLs.

• 3 URLs: ~10,666 tokens each (deep extraction)

• 5 URLs: ~6,400 tokens each (RECOMMENDED: balanced)

• 10 URLs: ~3,200 tokens each (detailed)

• 50 URLs: ~640 tokens each (quick scan)

Extraction Prompt Formula:

Extraction Rules:

• Use pipe | to separate extraction targets

• Minimum 3 targets required

• Be SPECIFIC about what you want ("pricing tiers" not "pricing")

• Include "with focus on" to prioritize certain aspects

• More targets = more comprehensive extraction

• Aim for 5-10 extraction targets

Extraction Templates by Domain:

Product Research:

Technical Documentation:

Competitive Analysis:

Example: ❌ BAD: {"urls": ["url"], "use_llm": false, "what_to_extract": "get pricing"} → raw HTML, vague prompt, 1 target, no focus areas

✅ GOOD: {"urls": [5 URLs], "use_llm": true, "what_to_extract": "Extract pricing tiers | plan features | API rate limits | enterprise options | integration capabilities | user testimonials with focus on enterprise features, API limitations, and real-world performance data"} → clean structured extraction

Pro Tips:

1. ALWAYS use use_llm=true - The AI extraction is the tool's superpower

2. Use 3-10 URLs - Balance between depth and breadth

3. Specify 5-10 extraction targets - More targets = more comprehensive

4. Use pipe - Clearly separate each target

5. Add focus areas - "with focus on X, Y, Z" for prioritization

6. Be specific - "pricing tiers" not "pricing", "API rate limits" not "API info"

7. Cover multiple aspects - Features, pricing, technical, social proof

Automatic Fallback: Basic → JavaScript rendering → JavaScript + US geo-targeting Batching: Max 30 concurrent requests (50 URLs = [30] then [20] batches)

REMEMBER: AI extraction costs pennies but saves hours of manual parsing!

🔥 WEB SEARCH - MINIMUM 3 KEYWORDS, RECOMMENDED 5-7

This tool searches up to 100 keywords IN PARALLEL via Google. Using 1-2 keywords = wasting the tool's parallel search power!

Results Budget: 10 results per keyword, all searches run in parallel.

• 3 keywords = 30 results (minimum)

• 7 keywords = 70 results (RECOMMENDED)

• 100 keywords = 1000 results (comprehensive)

7-Perspective Keyword Formula - Each keyword targets a DIFFERENT angle:

1. Direct/Broad: "[topic]" Example: "React state management"

2. Specific/Technical: "[topic] [technical term]" Example: "React useReducer vs Redux"

3. Problem-Focused: "[topic] issues/debugging/problems" Example: "React state management performance issues"

4. Best Practices: "[topic] best practices [year]" Example: "React state management best practices 2024"

5. Comparison: "[A] vs [B]" Example: "React state management libraries comparison"

6. Tutorial/Guide: "[topic] tutorial/guide" Example: "React state management tutorial"

7. Advanced: "[topic] patterns/architecture large applications" Example: "React state management patterns large applications"

Search Operators with Examples:

• site:domain.com - Search within specific site Example: "React hooks" site:github.com → React hooks repos on GitHub

• "exact phrase" - Match exact phrase Example: "Docker OOM" site:stackoverflow.com → exact error discussions

• -exclude - Exclude term from results Example: React state management -Redux → find alternatives to Redux

• filetype:pdf - Find specific file types Example: React tutorial filetype:pdf → downloadable guides

• OR - Match either term Example: React OR Vue state management → compare frameworks

Keyword Patterns by Use Case:

Technology Research: ["PostgreSQL vs MySQL performance 2024", "PostgreSQL best practices production", "\"PostgreSQL\" site:github.com stars:>1000", "PostgreSQL connection pooling", "PostgreSQL vs MongoDB use cases"]

Problem Solving: ["Docker container memory leak debugging", "Docker memory limit not working", "\"Docker OOM\" site:stackoverflow.com", "Docker memory optimization best practices"]

Comparison Research: ["Next.js vs Remix performance", "Next.js 14 vs Remix 2024", "\"Next.js\" OR \"Remix\" benchmarks", "Next.js vs Remix developer experience"]

Example: ❌ BAD: {"keywords": ["React"]} → 1 vague keyword, no operators, no diversity

✅ GOOD: {"keywords": ["React state management best practices", "React useReducer vs Redux 2024", "React Context API performance", "Zustand React state library", "\"React state\" site:github.com", "React state management large applications", "React global state alternatives -Redux"]} → 7 diverse angles with operators

Pro Tips:

1. Use 5-7 keywords minimum - Each reveals different perspective

2. Add year numbers - "2024", "2025" for recent content

3. Use search operators - site:, "exact", -exclude, filetype:

4. Vary specificity - Mix broad + specific keywords

5. Include comparisons - "vs", "versus", "compared to", "OR"

6. Target sources - site:github.com, site:stackoverflow.com

7. Add context - "best practices", "tutorial", "production", "performance"

8. Think parallel - Each keyword searches independently

Workflow: web_search → sequentialthinking (evaluate which URLs look promising) → scrape_links (MUST scrape promising URLs - that's where real content is!) → sequentialthinking (evaluate scraped content) → OPTIONAL: web_search again if gaps found → synthesize

Why this workflow works:

• Search results reveal new keywords you didn't think of

• Scraped content shows what's actually useful vs what looked good

• Thinking between tool calls prevents tunnel vision

• Iterative refinement = comprehensive coverage

CRITICAL:

• ALWAYS scrape after web_search - that's where the real content is!

• ALWAYS think between tool calls - evaluate and refine!

• DON'T stop after one search - iterate based on learnings!

FOLLOW-UP: Use scrape_links to extract full content from promising URLs!