🧠 Model Context Prompt - MCP

Step 1: Create Your Research Plan

Before using AI, establish your research framework:

Define Your Scope:

  • What specific problem are you investigating?
  • Who is the affected population?
  • What geographic area?
  • What time frame?

Set Research Objectives:

  • Understand likely root causes and effects
  • Identify common indicators and measurement approaches
  • Find credible sources for deeper reading
  • Surface knowledge gaps and assumptions
  • Prepare questions for stakeholder validation

Plan Documentation:

  • How will you track sources and citations?
  • Where will you store evidence vs. assumptions?
  • How will you organize findings for team review?

Step 2: Build Your MCP (Model Context Protocol) Prompt

The MCP structure ensures your AI assistant provides systematic, high-quality outputs:

2.1 System Context Block 

You are an evidence-focused research assistant for nonprofits/social enterprises.
Behaviors: neutral, transparent, concise. Cite publisher + year. Prefer reputable 
sources (UN agencies, government statistics, multilaterals, peer-reviewed). 
Separate facts from assumptions. Flag uncertainties and potential bias.

2.2 Knowledge Context Block 

Draft core problem: [Your one-line problem statement]
Scope/Context: [Geography, population, timeframe if relevant]
What we already know: 
- [Existing insight 1 with source if available]
- [Existing insight 2]
- [Existing insight 3]
Keywords/aliases: [Related terms and concepts]

2.3 Task Context Block 

Objectives:
1) Propose a Preliminary Problem Tree: core problem; 2-3 levels of root causes; key effects
2) Suggest common indicators that evidence each cause/effect (name • unit • typical sources)
3) Provide 5-10 credible sources to read next (publisher • year • link if available)
4) List uncertainties/gaps and assumptions that need field validation
5) Draft 10 stakeholder research questions to validate and deepen understanding

2.4 Prompt Block 

Using the contexts above, produce:
- A **Preliminary Problem Tree** in markdown bullets
- A table of **indicators** (Indicator • What it measures • Possible sources)
- A **Reading list** (publisher • year • link if available)
- **Uncertainties & assumptions** (bullets)
- **10 stakeholder questions**

Step 3: Run and Quality-Check AI Output

Execute the MCP:

  • Paste complete prompt into ChatGPT, Claude, or similar AI assistant
  • Review all outputs systematically
  • Don’t accept everything at face value

Quality Verification Checklist:

  • Sources credible? (UN agencies, government stats, peer-reviewed, reputable NGOs)
  • Recent enough? (Appropriate recency for your topic and context)
  • Links functional? (Do they open and match the described content?)
  • Context fit? (Do findings match your geography and population?)
  • Evidence vs. assumptions clear? (Are claims supported or speculative?)
  • Contradictions noted? (Are there conflicting sources or findings?)

Spot-Check Process:

  • Open 2-3 cited sources to verify dates, relevance, and accuracy
  • Cross-reference key statistics with original sources
  • Note any discrepancies between AI summary and source content

Step 4: Build Your Preliminary Problem Tree

Organize AI outputs into tree structure using the Problem Tree Template and Use Cases:

Core Problem:

  • Extract and refine the central problem statement
  • Ensure it’s specific, measurable, and solution-neutral
  • Specify affected population and location

Root Causes:

  • Organize causes by levels (immediate, underlying, structural)
  • Group into logical categories (economic, social, policy, etc.)
  • Tag each item: (E) for evidence-based, (A) for assumption

Effects:

  • Sort into time horizons (immediate, medium-term, long-term)
  • Consider different impact levels (individual, community, system)
  • Include both direct and indirect consequences

Documentation:

  • Keep citation list for evidence-based items
  • Note specific sources for key statistics or claims
  • Track which findings need stakeholder validation

📥 Download MCP Resources

Model Context Prompt Toolkit

🚀 Next Steps

  1. Choose an MCP template appropriate for your current problem tree needs
  2. Customize the parameters for your specific context and requirements
  3. Conduct AI-enhanced research following the workflow guidelines
  4. Continue to ⭐ Quality Assurance Checklist to validate your research
  5. Use 🌳 Problem Tree Template to structure your findings
  6. Prepare for stakeholder engagement using AI insights as starting hypotheses

AI is a powerful research accelerator, but it’s not a replacement for critical thinking and community engagement. Use MCP to get to better questions faster, then validate everything with people who live the reality you’re trying to understand.