strategy-mcp

Professional-grade product strategy frameworks as MCP tools.

Give any MCP-compatible AI assistant — Claude Code, Cursor, Cline — instant access to 12 structured strategy frameworks. Not templates. Not prompts. Actual tools that accept your inputs, apply the framework, show the reasoning, and return specific next steps.

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Why this exists

AI tools are great at generating content. They're inconsistent at applying structured thinking.

strategy-mcp is the product management layer that's been missing from the AI toolkit. Every framework a PM reaches for — RICE scoring, Jobs-to-be-Done, competitive positioning, OKR generation — encoded as a tool your AI can use natively.

Each tool returns:

• Structured analysis with reasoning (not just a score)
• 2-5 actionable next steps (not generic advice)
• Confidence indicator (High / Medium / Low) with rationale
• Pressure-test questions to challenge the analysis

Built by Sohaib Thiab — former CPO, now building AI products in public.

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Install in 60 seconds

strategy-mcp is published on PyPI — install it with a single command.

Claude Code

claude mcp add strategy-mcp -- uv run --with strategy-mcp python server.py

Cursor

Add to your Cursor MCP settings (.cursor/mcp.json):

{
  "mcpServers": {
    "strategy-mcp": {
      "command": "uv",
      "args": ["run", "--with", "strategy-mcp", "python", "server.py"]
    }
  }
}

Cline

Add to your Cline MCP settings:

{
  "mcpServers": {
    "strategy-mcp": {
      "command": "uv",
      "args": ["run", "--with", "strategy-mcp", "python", "server.py"]
    }
  }
}

Plain pip

pip install strategy-mcp

Run locally (development)

git clone https://github.com/sohaibt/strategy-mcp.git
cd strategy-mcp
uv run python server.py

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The 12 Tools

Prioritization

Tool	What it does
rice_score	Score a feature using Reach, Impact, Confidence, Effort. Returns a calculated score, priority tier, and factor-by-factor analysis.

Discovery

Tool	What it does
assumption_map	Map assumptions into a 2x2 matrix of confidence vs. impact. Identifies your riskiest bets and what to test first.
jobs_to_be_done	Analyze a feature through the JTBD lens — job statement, functional/emotional/social dimensions, hiring criteria, switching barriers.

Positioning

Tool	What it does
competitive_position	Map your product and competitors on a 2-axis chart. Identifies nearest threats, white space, and differentiation opportunities.

Business Model

Tool	What it does
business_model_review	Assess a business model using the Business Model Canvas. Reviews all 9 components for clarity, gaps, and coherence.
tam_sam_som	Estimate addressable market tiers with top-down + bottom-up cross-validation. Includes sanity checks and key assumptions.
pricing_strategy	Analyze pricing against positioning and the competitive landscape. Recommends a model, price range, and flags risks.

Execution

Tool	What it does
okr_generator	Generate well-formed OKRs from a strategic goal. Creates an inspirational objective with 3-5 measurable key results.
initiative_scoper	Break a strategic goal into scoped initiatives with dependencies, effort estimates, critical path, and execution sequence.

Advanced

Tool	What it does
wardley_assessment	Assess where components sit on the evolution axis (Genesis → Commodity). Recommends build vs. buy for each.
hypothesis_builder	Transform assumptions into structured, testable hypotheses with success metrics, test methods, and risk assessment.
decision_log_entry	Structure a product decision for archiving — captures context, alternatives, rationale, and revisit conditions.

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Example: RICE Scoring

Ask your AI assistant:

"Score our new AI onboarding feature using RICE. It reaches about 5,000 users per quarter, high impact, we're 80% confident, and it'll take 2 person-months."

What you get back:

{
  "feature_name": "AI Onboarding Feature",
  "rice_score": 2000.0,
  "priority_tier": "Critical",
  "score_breakdown": "RICE = (Reach x Impact x Confidence) / Effort\n     = (5,000 x 1 x 0.8) / 2\n     = 2,000.0",
  "analysis": "**AI Onboarding Feature** scores **2,000.0** — classified as **Critical** priority.\n\n- **Reach is moderate** (5,000 users/quarter)\n- **Impact is medium** (1x) per user affected.\n- **Confidence is high** (80%)\n- **Effort is moderate** (2 person-months)",
  "next_steps": [
    "Prioritize AI Onboarding Feature in the next sprint/cycle — the score supports it.",
    "Define success metrics before building so you can validate the impact estimate post-launch.",
    "Stack-rank this against your top 5 backlog items using the same RICE framework for consistency."
  ],
  "confidence": "High",
  "confidence_rationale": "The input estimates appear data-informed (high confidence %, meaningful reach).",
  "pressure_test_questions": [
    "Is the reach estimate (5,000 users/quarter) based on actual data or a gut feeling?",
    "Would the impact really be medium? What's the evidence from user research?",
    "Are there hidden dependencies that could inflate the 2-month effort estimate?"
  ]
}

Every tool follows this same structure: analysis + next steps + confidence + pressure-test questions.

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Example: Business Model Canvas Review

"Review the business model for my AI analytics startup. We target mid-market SaaS companies, our value prop is real-time anomaly detection..."

The tool assesses all 9 BMC components, rates each as Strong/Adequate/Weak/Missing, identifies critical gaps, evaluates coherence between components, and tells you exactly what to fix first.

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Example: Hypothesis Builder

"I have three assumptions about my product. Turn them into testable hypotheses."

Each assumption becomes a structured hypothesis with: independent/dependent variables, success metric, success threshold, suggested test method, estimated duration, and risk assessment. Hypotheses are prioritized by risk level — test the scariest ones first.

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How it works

strategy-mcp is a stateless MCP server built with FastMCP. Each tool:

1. Accepts structured inputs via MCP tool parameters
2. Applies the framework logic in Python (no external API calls)
3. Returns structured JSON that your AI assistant formats into a readable response

All analysis is done locally using your inputs + the framework logic. No data leaves your machine. No API keys required.

Architecture

Your AI Assistant (Claude/Cursor/Cline)
        │
        ▼
   MCP Protocol
        │
        ▼
  strategy-mcp server (FastMCP)
        │
        ├── tools/prioritization.py    → RICE scoring
        ├── tools/discovery.py         → Assumption map, JTBD
        ├── tools/positioning.py       → Competitive positioning
        ├── tools/business_model.py    → BMC, TAM/SAM/SOM, Pricing
        ├── tools/execution.py         → OKR generator, Initiative scoper
        ├── tools/advanced.py          → Wardley assessment, Hypothesis builder
        └── tools/governance.py        → Decision log

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Development

Run tests

uv run pytest tests/ -v

All 12 tools have happy-path and edge-case tests (24 tests total).

Project structure

strategy-mcp/
├── server.py              # MCP server entry point
├── tools/
│   ├── prioritization.py  # RICE score
│   ├── discovery.py       # Assumption map, JTBD
│   ├── positioning.py     # Competitive positioning
│   ├── business_model.py  # BMC, TAM/SAM/SOM, Pricing
│   ├── execution.py       # OKR generator, Initiative scoper
│   ├── advanced.py        # Wardley assessment, Hypothesis builder
│   └── governance.py      # Decision log
├── schemas/
│   └── models.py          # Pydantic models for all inputs/outputs
├── tests/
│   └── test_tools.py      # 24 tests across all 12 tools
└── pyproject.toml

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Contributing

Contributions welcome! Some ways to help:

• Add a new framework tool — open an issue with the framework name and what it should do
• Improve an existing tool — better analysis logic, smarter recommendations
• Add test cases — especially edge cases and realistic product scenarios
• Fix bugs — if a tool gives bad advice, that's a bug

Please open an issue before submitting large PRs so we can discuss the approach.

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License

MIT — use it however you want.

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Built by

Sohaib Thiab — Former CPO, now building AI products in public.

• Mastering Product HQ — Weekly writing on product leadership

Want connected strategy execution? GetVelocity.ai takes these frameworks further — connecting your OKRs to Jira, Linear, and ClickUp with AI-powered monitoring and real-time velocity tracking.

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If strategy-mcp saves you a bad decision, it's done its job.