Skip to main content
Glama
yigitkonur

Research Powerpack MCP

by yigitkonur
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

deep_research

Submit 2-10 detailed research questions to be investigated in parallel by AI, with dynamic token allocation for comprehensive multi-perspective analysis.

Instructions

šŸ”„ 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. šŸŽÆ WHAT I NEED: Clearly state what you're trying to achieve or understand

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

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

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

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

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

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

ATTACH FILES when asking about code - THIS IS MANDATORY:

  • šŸ› Bugs/errors ā†’ Attach the failing code

  • āš” Performance issues ā†’ Attach the slow code paths

  • ā™»ļø Refactoring ā†’ Attach current implementation

  • šŸ” Code review ā†’ Attach code to review

  • šŸ—ļø Architecture ā†’ Attach relevant modules

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

Example: āŒ BAD: {"questions": [{"question": "Research React hooks"}]} ā†’ 1 vague question, no template, no context, wastes 90% capacity

āœ… GOOD: 

{"questions": [{
  "question": "šŸŽÆ WHAT I NEED: Understand when to use useCallback vs useMemo in React 18\n\nšŸ¤” WHY: Optimizing a data-heavy dashboard with 50+ components, seeing performance issues\n\nšŸ“š WHAT I KNOW: Both memoize values, useCallback for functions, useMemo for computed values. Unclear when each actually prevents re-renders.\n\nšŸ”§ HOW I'LL USE THIS: Refactor Dashboard.tsx to eliminate unnecessary re-renders\n\nā“ SPECIFIC QUESTIONS:\n1. When does useCallback actually prevent re-renders vs when it doesn't?\n2. Performance benchmarks: useCallback vs useMemo vs neither in React 18?\n3. Common anti-patterns that negate their benefits?\n4. How to measure if they're actually helping?\n\nšŸŒ PRIORITY: Official React docs, React team blog posts\nāš” FOCUS: Patterns for frequently updating state"
}, ...add 4 more questions for comprehensive coverage]}

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) ā†’ sequentialthinking (evaluate, identify gaps) ā†’ OPTIONAL: deep_research AGAIN with NEW questions based on learnings ā†’ sequentialthinking (synthesize) ā†’ 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!

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionsYes**Batch deep research (2-10 questions) with dynamic token allocation.** **TOKEN BUDGET:** 32,000 tokens distributed across all questions: - 2 questions: 16,000 tokens/question (deep dive) - 5 questions: 6,400 tokens/question (balanced) - 10 questions: 3,200 tokens/question (rapid multi-topic) **WHEN TO USE:** - Need multi-perspective analysis on related topics - Researching a domain from multiple angles - Validating understanding across different aspects - Comparing approaches/technologies side-by-side **EACH QUESTION SHOULD INCLUDE:** - Topic & context (what decision it informs) - Your current understanding (to fill gaps) - Specific sub-questions (2-5 per topic) **USE:** Maximize question count for comprehensive coverage. All questions run in parallel. Group related questions for coherent research.

Tool Definition Quality

A4.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It explains parallel execution, token distribution (32,000 total), and mandatory file attachments for code questions. However, it does not explicitly state failure modes (e.g., token overrun) or output format, leaving minor gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well-organized with headings, bullet points, examples, and pro tips. It front-loads key information (parallelism, recommended count) and uses markdown for clarity. Some redundancy (e.g., 'recommended 5+' is repeated) could be trimmed without losing substance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (batch research, token budget, file attachments) and absence of output schema, the description covers input format, usage constraints, file attachment requirements, and iterative workflow. It includes comprehensive examples and edge cases (e.g., scope expansion triggers).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3, but the description vastly enriches the single parameter (questions) with a detailed structured template, examples, file attachment guidance, and token allocation details. This goes well beyond the schema's own description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool runs 2-10 questions in parallel for AI-powered research, and distinguishes it from sibling tools (e.g., web_search, search_reddit) by focusing on multi-perspective, batch research with a shared token budget.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use scenarios (multi-perspective analysis, domain research from multiple angles) and includes a suggested workflow with sequentialthinking. It also gives concrete examples and recommends 5-10 questions to maximize capacity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/yigitkonur/research-powerpack-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server