Lesson 1.1: Problem Tree Analysis

Model Context Protocol (MCP)

The four-block framework for focused, credible AI prompts.

The Four-Block MCP Structure

MCP organizes your prompt into four blocks:

Block 1: System Context

Block 2: Knowledge Context

Block 3: Task Context

Block 4: Final Prompt

How MCP Blocks Flow Together

graph TD
    %% INPUT BLOCKS
    B1["📋 BLOCK 1: System Context<br/>Define the AI's Role<br/>• Evidence-focused<br/>• Neutral & transparent<br/>• Cite sources"]
    B2["🧠 BLOCK 2: Knowledge Context<br/>Share What You Know<br/>• Draft problem statement<br/>• Geographic scope<br/>• Existing insights"]
    B3["🎯 BLOCK 3: Task Context<br/>Define Your Objectives<br/>• Problem Tree structure<br/>• Indicator suggestions<br/>• Reading list<br/>• Uncertainties"]
    B4["💬 BLOCK 4: Final Prompt<br/>Clear Instruction<br/>Tie everything together<br/>Request specific outputs"]

    %% PROCESSING
    PROCESS["🤖 AI PROCESSING<br/>Structured Analysis"]

    %% OUTPUT CATEGORIES
    OUT1["📊 Preliminary<br/>Problem Tree"]
    OUT2["📈 Indicator<br/>Suggestions"]
    OUT3["📚 Reading List<br/>with Citations"]
    OUT4["❓ Uncertainties &<br/>Assumptions"]
    OUT5["💡 Stakeholder<br/>Questions"]

    %% Flow relationships
    B1 --> PROCESS
    B2 --> PROCESS
    B3 --> PROCESS
    B4 --> PROCESS

    PROCESS --> OUT1
    PROCESS --> OUT2
    PROCESS --> OUT3
    PROCESS --> OUT4
    PROCESS --> OUT5

    %% Festa Design System Colors
    style B1 fill:#F59E0B,stroke:#D97706,stroke-width:2px,color:#1F2937
    style B2 fill:#F59E0B,stroke:#D97706,stroke-width:2px,color:#1F2937
    style B3 fill:#F59E0B,stroke:#D97706,stroke-width:2px,color:#1F2937
    style B4 fill:#F59E0B,stroke:#D97706,stroke-width:2px,color:#1F2937
    style PROCESS fill:#10B981,stroke:#059669,stroke-width:3px,color:#fff
    style OUT1 fill:#72B043,stroke:#5A8F36,stroke-width:2px,color:#fff
    style OUT2 fill:#72B043,stroke:#5A8F36,stroke-width:2px,color:#fff
    style OUT3 fill:#72B043,stroke:#5A8F36,stroke-width:2px,color:#fff
    style OUT4 fill:#72B043,stroke:#5A8F36,stroke-width:2px,color:#fff
    style OUT5 fill:#72B043,stroke:#5A8F36,stroke-width:2px,color:#fff

Four inputs, five outputs.

Block 1: System Context (Define the AI's Role)

Sets the AI's role and quality standards.

Block 1 - System Context 
You are an evidence-focused research assistant for nonprofits and social enterprises.

Behaviors: Be neutral, transparent, and concise. Always cite publisher + year for sources.
Prefer reputable sources (UN agencies, government statistics, peer-reviewed research,
established NGOs). Clearly separate facts from assumptions. Flag uncertainties and
potential bias in sources.

Why this matters: AI defaults to generic content without this framing.

Block 2: Knowledge Context (Share What You Know)

Focuses AI on gaps, not what you already know.

Block 2 - Knowledge Context 
Draft core problem: [Your 1-2 sentence problem statement]
Geographic/population scope: [Location, demographic details if relevant]

What we already know:
• [Existing insight 1 with source if you have it]
• [Existing insight 2]
• [Existing insight 3]

Keywords and related terms: [List of relevant terms, synonyms, related concepts]

Example:

Example - Knowledge Context 
Draft core problem: Young adults aged 18-25 in rural Kenya have limited access to
decent employment opportunities.

Geographic/population scope: Rural areas of Nyanza region, Kenya. Youth 18-25 years old,
mixed gender.

What we already know:
• National youth unemployment rate ~20% (Kenya National Bureau of Statistics, 2022)
• Agriculture is declining as primary livelihood
• Some anecdotal evidence of skills-job mismatch

Keywords: youth unemployment, rural livelihoods, skills gap, Kenya Nyanza,
agricultural transition, informal sector

Block 3: Task Context (Define Your Objectives)

Tells AI exactly what outputs you need.

Block 3 - Task Context 
Research objectives:

1) Create a preliminary Problem Tree: core problem statement; 2-3 levels of root causes;
   key effects/consequences

2) Suggest measurable indicators for each major cause and effect (indicator name • unit
   of measurement • typical data sources)

3) Provide 5-10 credible sources for deeper reading (publisher • year • link if available)

4) Identify uncertainties, knowledge gaps, and assumptions that need stakeholder validation

5) Draft 10 targeted questions for stakeholder interviews/focus groups

Why 5 objectives? One request gets the tree, indicators, sources, gaps, and questions.

Block 4: Final Prompt (Clear Instruction)

Gives a clear instruction on format.

Block 4 - Final Prompt 
Using all the context provided above, generate:

- **Preliminary Problem Tree** in clear markdown format
- **Indicator suggestions** in table format
- **Recommended reading list** with full citations
- **List of uncertainties and assumptions** requiring validation
- **10 stakeholder validation questions** that are open-ended and non-leading

Why specify format? Markdown outputs are immediately usable.

Complete MCP Template (Copy & Customize)

Complete MCP Template - Ready to Use 
## SYSTEM CONTEXT
You are an evidence-focused research assistant for nonprofits and 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.

## KNOWLEDGE CONTEXT
Draft core problem: {{Insert your problem statement}}
Scope/Context: {{Geography, population, timeframe}}
What we already know:
• {{Existing knowledge point 1}}
• {{Existing knowledge point 2}}
• {{Existing knowledge point 3}}
Keywords/aliases: {{Related terms}}

## TASK CONTEXT
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

## PROMPT
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**

Quality Assurance for AI Outputs

Verify every output:

graph TD
    %% Start
    START["Receive AI Output<br/>from MCP Prompt"]

    %% CHECKPOINT 1: Source Credibility
    CHECK1{"Source Credibility<br/>Check"}
    Q1A["Are sources reputable?<br/>UN, gov stats,<br/>peer-reviewed, NGOs"]
    Q1B["Are they recent<br/>enough for context?"]
    Q1C["Do links work and<br/>match descriptions?"]
    FAIL1["⚠️ Flag Issues<br/>Request new sources<br/>or verify manually"]

    %% CHECKPOINT 2: Context Fit
    CHECK2{"Context Fit<br/>Assessment"}
    Q2A["Do findings match<br/>your geography/<br/>population?"]
    Q2B["Are cultural & economic<br/>contexts appropriate?"]
    Q2C["Any obvious<br/>contradictions?"]
    FAIL2["⚠️ Flag Mismatches<br/>Adjust MCP prompt<br/>or seek local sources"]

    %% CHECKPOINT 3: Evidence Clarity
    CHECK3{"Evidence vs<br/>Assumption Check"}
    Q3A["Are claims<br/>properly supported?"]
    Q3B["Are limitations<br/>acknowledged?"]
    Q3C["Is bias potential<br/>noted?"]
    FAIL3["⚠️ Tag Assumptions<br/>Mark for stakeholder<br/>validation"]

    %% Success Path
    SUCCESS["✅ Verified Output<br/>Tag evidence (E)<br/>Tag assumptions (A)<br/>Build Problem Tree"]

    %% Relationships
    START --> CHECK1
    CHECK1 -->|Review| Q1A
    Q1A -->|Pass| Q1B
    Q1B -->|Pass| Q1C
    Q1C -->|Pass| CHECK2
    Q1A -->|Fail| FAIL1
    Q1B -->|Fail| FAIL1
    Q1C -->|Fail| FAIL1
    FAIL1 --> START

    CHECK2 -->|Review| Q2A
    Q2A -->|Pass| Q2B
    Q2B -->|Pass| Q2C
    Q2C -->|Pass| CHECK3
    Q2A -->|Fail| FAIL2
    Q2B -->|Fail| FAIL2
    Q2C -->|Fail| FAIL2
    FAIL2 --> START

    CHECK3 -->|Review| Q3A
    Q3A -->|Pass| Q3B
    Q3B -->|Pass| Q3C
    Q3C -->|Pass| SUCCESS
    Q3A -->|Concerns| FAIL3
    Q3B -->|Concerns| FAIL3
    Q3C -->|Concerns| FAIL3
    FAIL3 --> SUCCESS

    %% Festa Design System Colors
    %% Start - Neutral
    style START fill:#6B7280,stroke:#4B5563,stroke-width:2px,color:#fff

    %% Checkpoints - Pot of Gold (decision points)
    style CHECK1 fill:#F59E0B,stroke:#D97706,stroke-width:2px,color:#1F2937
    style CHECK2 fill:#F59E0B,stroke:#D97706,stroke-width:2px,color:#1F2937
    style CHECK3 fill:#F59E0B,stroke:#D97706,stroke-width:2px,color:#1F2937

    %% Questions - Leaf (evaluation criteria)
    style Q1A fill:#72B043,stroke:#5A8F36,stroke-width:1px,color:#fff
    style Q1B fill:#72B043,stroke:#5A8F36,stroke-width:1px,color:#fff
    style Q1C fill:#72B043,stroke:#5A8F36,stroke-width:1px,color:#fff
    style Q2A fill:#72B043,stroke:#5A8F36,stroke-width:1px,color:#fff
    style Q2B fill:#72B043,stroke:#5A8F36,stroke-width:1px,color:#fff
    style Q2C fill:#72B043,stroke:#5A8F36,stroke-width:1px,color:#fff
    style Q3A fill:#72B043,stroke:#5A8F36,stroke-width:1px,color:#fff
    style Q3B fill:#72B043,stroke:#5A8F36,stroke-width:1px,color:#fff
    style Q3C fill:#72B043,stroke:#5A8F36,stroke-width:1px,color:#fff

    %% Fail paths - Chicken Comb (issues to address)
    style FAIL1 fill:#E12729,stroke:#B91C1C,stroke-width:2px,color:#fff
    style FAIL2 fill:#E12729,stroke:#B91C1C,stroke-width:2px,color:#fff
    style FAIL3 fill:#FCD34D,stroke:#F59E0B,stroke-width:2px,color:#1F2937

    %% Success - Pepper Green (validated output)
    style SUCCESS fill:#10B981,stroke:#059669,stroke-width:3px,color:#fff

Red Flags to Watch For

❌

Generic responses that could apply anywhere

Too vague to be useful.

❌

Outdated sources

Check the publication date.

❌

Broken or suspicious links

AI sometimes invents plausible citations. Verify.

❌

Claims without citations

No source named? Tag it (A).

❌

Overly confident assertions

Good research admits gaps.

Tips for Better MCP Prompts

  • Be specific: name the population, region, and theme
  • Include what you already know
  • Request specific formats: bullets, tables, or lists
  • Iterate if the output is too general
Lesson 1.1: Problem Tree Analysis 55%
Step 6 of 11 · Model Context Protocol