New: Boardroom MCP Engine!

Boardroom MCP Documentation

From zero to a fully governed AI agent in 5 minutes. This guide takes you from the free demo council all the way through advanced cognitive protocols like Mind Versioning and the Prometheus Protocol.

πŸš€

Quick Start

FREE

Get your first boardroom consultation in under 2 minutes. The free server ships with a demo council of 3 named advisors (Warren Buffett, Linus Torvalds, Marcus Aurelius) β€” no API keys, no accounts, no configuration.

1. Add to your MCP client

Open your Claude Desktop config (or Cursor, Windsurf, etc.) and add the boardroom server:

claude_desktop_config.json
{
  "mcpServers": {
    "boardroom": {
      "command": "npx",
      "args": ["-y", "boardroom-mcp"]
    }
  }
}

2. Ask your agent a question

Now just ask your AI agent to consult the boardroom:

Your prompt
"Board: Should I build a mobile app or a progressive web app 
for my new SaaS product?"

3. What you get back

The boardroom returns structured analysis from multiple advisors β€” each with a distinct perspective, confidence score, and recommendation. The demo council includes:

Warren Buffett

Business strategy, competitive moats, and long-term value

Linus Torvalds

Technology architecture, simplicity, and maintainability

Marcus Aurelius

Values alignment, ethical judgment, and Stoic wisdom

πŸ“¦

Installation

FREE

Follow the step-by-step guide for your AI tool below. No coding experience required β€” just copy, paste, and restart.

⚑ Before you start β€” check Node.js

Boardroom MCP needs Node.js 18 or higher. Open a terminal and run:

Terminal
node --version

You should see something like v22.12.0. If you see an error or a version below 18, install Node.js from nodejs.org (choose the LTS version).

Choose your AI tool:

🟣Claude Desktop β†—MOST POPULAR

Step 1: Find the config file

macOS: Open Finder β†’ Go β†’ Go to Folder β†’ paste: ~/Library/Application Support/Claude/

Windows: Press Win+R β†’ paste: %APPDATA%\Claude\

Linux: ~/.config/Claude/

Step 2: Edit claude_desktop_config.json

Open (or create) claude_desktop_config.json and paste this entire block:

claude_desktop_config.json
{
  "mcpServers": {
    "boardroom": {
      "command": "npx",
      "args": ["-y", "boardroom-mcp"]
    }
  }
}

πŸ’‘ If the file already has other servers, just add the "boardroom": {...} block inside the existing mcpServers object (don't forget a comma after the previous entry).

Step 3: Restart Claude Desktop

Fully quit Claude Desktop (not just close the window β€” right-click the dock/taskbar icon β†’ Quit) and reopen it.

Step 4: Test it!

Type this into Claude:

Your prompt
Use the analyze tool with task: "Test β€” is the Boardroom working?"

βœ… If you see a response with advisor names and recommendations, you're all set!

One command β€” that's it:

Terminal
claude mcp add boardroom -- npx -y boardroom-mcp

This registers the Boardroom as an MCP server. Now just ask Claude Code:

Your prompt
Use the analyze tool with task: "Test β€” is the Boardroom working?"

πŸ–±οΈCursor β†—

Step 1: Create the config file

In your project's root folder, create a file called .cursor/mcp.json and paste:

.cursor/mcp.json
{
  "mcpServers": {
    "boardroom": {
      "command": "npx",
      "args": ["-y", "boardroom-mcp"]
    }
  }
}

Step 2: Restart Cursor

Close and reopen Cursor, or press Cmd+Shift+P β†’ "Reload Window".

Step 3: Test in Agent mode

Open the AI chat panel (Cmd+L), switch to Agent mode, and type your question.

πŸ’œVS Code β†—(GitHub Copilot)

Step 1: Enable MCP in Copilot

Go to Settings β†’ Extensions β†’ Copilot and enable MCP support.

Step 2: Create the config file

Create .vscode/mcp.json in your project root. ⚠️ Note: VS Code uses servers, not mcpServers.

.vscode/mcp.json
{
  "servers": {
    "boardroom": {
      "command": "npx",
      "args": ["-y", "boardroom-mcp"]
    }
  }
}

Step 3: Reload and test

Press Cmd+Shift+P β†’ "Reload Window". Then open Copilot Chat in Agent mode and ask your question.

Step 1: Open MCP settings

Go to Settings β†’ MCP and click "Add Server".

Step 2: Add the config

Paste this JSON:

Windsurf MCP Config
{
  "mcpServers": {
    "boardroom": {
      "command": "npx",
      "args": ["-y", "boardroom-mcp"]
    }
  }
}

Step 3: Restart and test

Restart Windsurf and ask Cascade a question to verify.

πŸ€–ChatGPT Desktop β†—Plus/Pro required

Step 1: Open Connectors

In ChatGPT Desktop, go to Settings β†’ Developer β†’ Connectors.

Step 2: Add MCP server

Click "Add Connector" β†’ choose MCP β†’ set command to npx with args -y boardroom-mcp.

Step 3: Restart and test

Restart ChatGPT Desktop and ask a question.

One command:

Terminal
codex mcp add boardroom -- npx -y boardroom-mcp

Add to your config:

Create or edit .mcp.json in your workspace root:

.mcp.json
{
  "mcpServers": {
    "boardroom": {
      "command": "npx",
      "args": ["-y", "boardroom-mcp"]
    }
  }
}

Step 1: Clone and build

OpenClaw uses its skills system instead of MCP server configs. First, clone and build the server:

Terminal
git clone https://github.com/randysalars/boardroom-mcp.git
cd boardroom-mcp && npm install && npm run build

Step 2: Create the skill directory

Terminal
mkdir -p ~/.openclaw/skills/boardroom

Step 3: Create the skill file

Create ~/.openclaw/skills/boardroom/SKILL.md with this content:

~/.openclaw/skills/boardroom/SKILL.md
---
name: boardroom
description: Boardroom Mastermind Council β€” multi-advisor strategic analysis via MCP server.
metadata: {"clawdbot":{"emoji":"πŸ›οΈ","always":true,"requires":{"bins":["node"]},"primaryEnv":"BOARDROOM_ROOT"}}
---

# Boardroom MCP πŸ›οΈ

Multi-advisor strategic analysis. Run sessions via:

\`\`\`bash
BOARDROOM_ROOT=~/.ai/boardroom node /path/to/boardroom-mcp/dist/index.js
\`\`\`

Usage: "Board: Should I raise prices on my SaaS?"

Step 4: Add environment variable

Add to ~/.openclaw/.env:

~/.openclaw/.env
BOARDROOM_ROOT=/path/to/your/.ai/boardroom

Step 5: Restart and verify

Terminal
systemctl --user restart openclaw-gateway.service
openclaw skills list | grep boardroom

You should see: βœ“ ready β”‚ πŸ“¦ boardroom

πŸ”§ Alternative: Build from source

If npx doesn't work or you want to modify the code, you can clone and build from GitHub:

Terminal
git clone https://github.com/randysalars/boardroom-mcp.git
cd boardroom-mcp
npm install
npm run build
npm start

Then use the full path to the built file as the command in your MCP config instead of npx.

Environment Variables (Optional)

These are only needed if you're using the full protocol files (paid tier). The free demo works with zero configuration.

VariableDefaultDescription
BOARDROOM_ROOT~/.ai/boardroomPath to your protocol files directory (seat cards, LEDGER, Wisdom Codex)
BOARDROOM_TRUST_PATH~/.boardroom/trust-oracle.jsonPath to trust oracle data file
πŸ”§

The 5 Tools

FREE

The MCP server exposes 5 tools that your AI agent can call. Each tool handles a different aspect of AI governance β€” from multi-advisor debate to trust scoring.

analyze()

Consultation

Full boardroom consultation with multi-advisor synthesis. Routes your question to relevant advisors, loads their philosophies and decision criteria, searches LEDGER precedents and Wisdom Codex, and returns a structured markdown analysis with mandatory tension between opposing viewpoints.

Parameters

taskstringβ€” The decision, question, or task to analyze
analyze example
// Your AI agent calls this automatically when you say:
"Board: Should we migrate from REST to GraphQL?"

// It returns structured markdown:
# Boardroom Analysis
## Classification: technology
## Advisors Consulted
- **Linus Torvalds** β€” Technology architecture, simplicity
- **Warren Buffett** β€” Business strategy, competitive moats
- **Marcus Aurelius** β€” Values alignment, ethical judgment

## LEDGER Precedents
(matching past decisions)

## Wisdom Codex
(relevant distilled principles)

## Mandatory Tension
- **Torvalds:** Keep it simple, REST works fine
- **Buffett:** What creates the most long-term value?

check_governance()

Classification

Classifies a proposed task or action and provides governance routing β€” which councils should review it, what risk level it warrants, and any constitutional constraints. Fast classification without running a full boardroom session.

Parameters

taskstringβ€” The proposed task or action to classify
check_governance example
// Classify before acting:
"Use check_governance: Delete the production database backup"

// Returns markdown classification:
# Governance Check
**Decision Type:** technology
**Risk Level:** πŸ”΄ Critical
**Recommended Councils:** Technology, Risk
**Constitutional Flags:** Requires explicit human approval
**Recommended Action:** Full boardroom session before proceeding

query_intelligence()

Knowledge

Searches the LEDGER (past decisions) and the Wisdom Codex (distilled principles) for relevant precedents. This gives your agent institutional memory β€” it remembers what was decided before and why.

Parameters

querystringβ€” Search query β€” topic, keyword, or question
limitnumberβ€” Max results to return (default: 10)
query_intelligence example
// Search for past decisions:
"Use query_intelligence: pricing strategy"

// Returns matching LEDGER entries + wisdom:
# Intelligence Report
## LEDGER Matches (2 found)
- 2026-01-15: Adopted value-based pricing over cost-plus...
- 2026-02-01: Raised prices 30%, churn stayed flat...

## Wisdom Codex Matches (1 found)
- Price anchors: always show the most expensive option first...

trust_lookup()

Trust

Returns a 6-dimension trust vector for any entity β€” AI agent, tool, vendor, or platform. Dimensions: reliability, honesty, follow-through, outcome quality, stability, and risk profile. New entities return an "unknown" profile with bootstrap instructions.

Parameters

entitystringβ€” The entity to look up β€” agent name, tool, vendor, or platform
contextstringβ€” Optional context about how you are using this entity
trust_lookup example
// Look up trust for a vendor:
"Use trust_lookup for entity: Stripe, context: payment processing"

// Returns 6-dimension trust vector:
# Trust Lookup: Stripe
## 6-Dimension Trust Vector
| Dimension        | Score | Weight |
|------------------|-------|--------|
| Reliability      | 95%   | 25%    |
| Honesty          | 92%   | 20%    |
| Follow-Through   | 88%   | 20%    |
| Outcome Quality  | 90%   | 15%    |
| Stability        | 85%   | 10%    |
| Risk Profile     | 80%   | 10%    |

**Composite Score:** 89.5%
**Recommendation:** βœ… TRUST

report_outcome()

Learning

Logs the outcome of a past decision to the LEDGER. Creates institutional memory β€” your boardroom gets smarter over time as it learns from results. Returns a warning if the outcome could not be persisted to disk.

Parameters

taskstringβ€” The original task or decision
outcomestringβ€” What actually happened β€” result, success/failure, learnings
followedRecommendationbooleanβ€” Whether the Boardroom recommendation was followed (default: true)
report_outcome example
// Log what happened after a decision:
"Use report_outcome: task 'Migrated to GraphQL', 
  outcome 'Reduced API calls by 60% but increased complexity'"

// Returns confirmation:
# Outcome Recorded
βœ… Decision outcome has been logged to the Knowledge Flywheel.
**Task:** Migrated to GraphQL
**Outcome:** Reduced API calls by 60%, but increased complexity
**Followed Recommendation:** Yes
**Persisted:** Yes βœ…
πŸ’‘

Use Cases

FREE

The Boardroom works for any decision β€” from quick tech choices to high-stakes strategy. Here are real-world examples with the exact prompts to use.

πŸ—οΈ Architecture Decisions

β€œShould I build a mobile app or PWA for my SaaS?”

analyze
Your prompt
Use the analyze tool: "Should I build a native mobile app or a progressive web app for my B2B SaaS product? We have 2 developers and need to ship in 3 months."

Why it works: Buffett evaluates long-term competitive value, Torvalds assesses technical feasibility and maintenance burden, Aurelius asks if you're reacting to fear of missing out or acting from real user demand.

β€œMonolith vs microservices for a growing startup?”

analyze
Your prompt
Use the analyze tool: "We have 50K users and growing. Our monolith is getting hard to deploy. Should we start breaking it into microservices?"

Why it works: The boardroom prevents premature optimization β€” Torvalds will push back hard on unnecessary complexity while Buffett evaluates the cost of engineering time vs scaling risk.

πŸ’° Business Strategy

β€œShould I raise prices by 50%?”

analyze
Your prompt
Use the analyze tool: "I'm considering raising our SaaS price from $29/mo to $49/mo. We have 200 paying customers and low churn. Is this the right move?"

Why it works: Buffett applies margin-of-safety thinking to pricing, Aurelius challenges whether you're motivated by greed or genuine value delivery.

β€œShould I quit my job for my side project?”

analyze
Your prompt
Use the analyze tool: "My side project makes $4K/mo. My job pays $12K/mo. I have 8 months of savings. Should I go full-time on the side project?"

Why it works: This is where multi-advisor debate shines β€” the advisors will disagree and surface risks you haven't considered.

πŸ›‘οΈ Risk Assessment

β€œIs this deployment safe?”

check_governance
Your prompt
Use the check_governance tool: "Deploy new payment processing system to production on a Friday evening"

Why it works: Instant severity classification β€” this will flag as high-risk and recommend waiting for Monday. No debate needed, just fast classification.

β€œShould I give an AI agent database access?”

check_governance
Your prompt
Use the check_governance tool: "Grant our AI coding assistant read-write access to the production database for automated migrations"

Why it works: Constitutional checks will flag this as requiring explicit human approval, with risk factors around data integrity and blast radius.

🀝 Trust & Evaluation

β€œCan I trust this vendor?”

trust_lookup
Your prompt
Use the trust_lookup tool for entity: "Stripe" with context: "payment processing for our SaaS"

Why it works: Returns a 6-dimension trust vector (reliability, honesty, follow-through, outcome quality, stability, risk profile) with a composite score and recommendation.

β€œShould I hire this freelancer?”

trust_lookup
Your prompt
Use the trust_lookup tool for entity: "A freelance developer with 5-star reviews but no portfolio and a 2-month-old profile"

Why it works: The trust oracle will flag low transparency and missing track record despite surface-level signals.

πŸ“š Institutional Memory

β€œWhat did we decide about pricing before?”

query_intelligence
Your prompt
Use the query_intelligence tool: "pricing strategy decisions"

Why it works: Searches your LEDGER for past pricing decisions and the Wisdom Codex for distilled pricing principles β€” so you don't repeat past mistakes.

β€œLog what actually happened”

report_outcome
Your prompt
Use the report_outcome tool: task "Raised prices 30%", outcome "Revenue up 22%, churn stayed flat at 3%"

Why it works: Creates institutional memory β€” your boardroom gets smarter over time. Next time someone asks about pricing, this outcome is available as a precedent.

🎯 Everyday Decisions

β€œWhich feature should I build next?”

analyze
Your prompt
Use the analyze tool: "I have 3 feature requests: (1) dark mode, (2) API access, (3) team collaboration. We can only build one this quarter. Which one?"

Why it works: The boardroom forces structured prioritization instead of gut feeling β€” each advisor applies their domain lens to the same options.

β€œShould I open-source my project?”

analyze
Your prompt
Use the analyze tool: "Should I open-source my developer tool? It has 500 users and makes $2K/mo. I'm worried about clones but excited about community contributions."

Why it works: Buffett evaluates moat erosion, Torvalds champions open-source benefits, Aurelius asks what you'd be proud of regardless of outcome.

πŸ’‘ Pro tip: Chain tools together

Use check_governance first for quick severity classification, then query_intelligence to pull relevant precedents, then analyze for the full boardroom debate. After you act on the advice, use report_outcome to log what happened.

🎬 Example: Full Boardroom Session

Here's what you actually see when you ask the Boardroom a real question. This is the kind of output the analyze tool returns β€” multiple advisors with distinct philosophies, structured debate, and actionable synthesis.

Your prompt:

What you type
Board: My side project makes $4K/mo. My job pays $12K/mo. I have 8 months of savings. Should I go full-time on the side project?

What the Boardroom returns:

Boardroom Analysis β€” Full Session Output
# πŸ›οΈ Boardroom Analysis

## Classification
**Decision Type:** business-strategy
**Severity:** πŸ”΄ Critical (life-changing, irreversible in the short term)
**Councils Invoked:** Business Strategy, Risk Assessment, Values Alignment

---

## πŸ“‹ Advisors Consulted

### Warren Buffett β€” Business Strategy & Competitive Moats
> **Core philosophy:** Never risk what you have and need for what you don't have and don't need.
>
> **Position: PROCEED WITH CAUTION** ⚠️
>
> "Your side project is growing β€” $4K/month is real signal. But here's
> what I'd want to know: What's the trajectory? If it took 12 months
> to reach $4K, that's very different from 3 months. I'd want to see
> 3 consecutive months of growth before making the leap.
>
> **The math matters:** $8K/mo gap Γ— 8 months = $64K of runway without
> growth. That's not a safety margin, that's a countdown timer.
> I'd want 18 months of runway before I'd call it safe.
>
> **My recommendation:** Don't quit. Negotiate a 4-day work week or 
> contract your current role. Get the side project to $8K/mo first."

### Linus Torvalds β€” Technology & Execution
> **Core philosophy:** Talk is cheap. Show me the code.
>
> **Position: LEAN TOWARD GOING** 🟒
>
> "Stop overthinking. The real question isn't the money β€” it's whether
> this thing has technical legs. At $4K/mo you've proved market fit.
> What's holding the growth back β€” is it YOUR time?
>
> If the bottleneck is that you can't ship fast enough because of
> the day job, then staying employed is actively killing the project.
> Every day you're NOT working on it is a day a competitor could
> eat your lunch.
>
> **But:** Make sure you own the IP cleanly. Check your employment
> contract for moonlighting clauses and IP assignment."

### Marcus Aurelius β€” Values & Philosophical Alignment
> **Core philosophy:** The impediment to action advances action. What stands in the way becomes the way.
>
> **Position: ASK A DEEPER QUESTION** πŸ€”
>
> "Before you solve the logistics, answer this: If the side project
> failed completely in 6 months and you had to find a new job β€” would
> you regret trying? Or would you regret NOT trying?
>
> Fear of financial loss is rational. But there is another fear
> hiding behind it: the fear of discovering that your project
> ISN'T as good as you hope. Staying employed lets you avoid that
> reckoning indefinitely.
>
> **The Stoic test:** Imagine it's 5 years from now. Which decision
> will you respect yourself for making? That's your answer."

---

## βš”οΈ Mandatory Tension

| Advisor | Position | Key Risk They See |
|---------|----------|-------------------|
| Buffett | ⚠️ Wait | Insufficient runway, no growth guarantee |
| Torvalds | 🟒 Go | Day job is the bottleneck killing growth |
| Aurelius | πŸ€” Deeper | Decision is about identity, not just money |

**The core disagreement:** Buffett sees this as a financial optimization problem
(wait for more data). Torvalds sees it as an execution problem (the job is the
obstacle). Aurelius reframes entirely β€” this is a question about who you want to be.

---

## πŸ“š LEDGER Precedents (1 match)
- **2026-01-20:** "Whether to pursue full-time contracting vs employment"
  β†’ Outcome: Took contract, revenue doubled in 60 days. Key learning:
  the flexibility itself was the competitive advantage.

## πŸ“– Wisdom Codex (2 matches)
- *"Irreversible decisions deserve 10x the analysis of reversible ones.
  Quitting a job is semi-reversible β€” you can get another job, but
  not the same one."*
- *"Revenue β‰  profit. $4K/mo with $1K in costs and no benefits is $3K.
  Compare that to $12K + health insurance + 401K match."*

---

## 🎯 CEO Synthesis

**Recommended Path:** Hybrid transition over 90 days.

1. **Immediately:** Negotiate reduced hours or 4-day week at current job
2. **Next 30 days:** Validate growth trajectory at increased effort
3. **Day 60:** If side project hits $6K/mo, give notice
4. **Day 90:** Full transition with 6+ months of true runway

**Why this path:** It resolves Buffett's runway concern, tests Torvalds'
bottleneck theory without burning bridges, and gives you Aurelius' answer
through action rather than analysis.

**Risk watchout:** If your employer says no to reduced hours, that IS your
answer β€” they've made the decision for you.

Note: This is a representative demo-mode output with 3 advisors. The full protocol files (paid tier) unlock 450+ advisors across 38 expert councils β€” each with unique philosophies, decision criteria, veto power, and advice styles. Industry-specific advisors are routed automatically based on your question.

πŸ“‹

Quick Command Reference

FREE

Copy-paste these prompts into your AI chat to use each tool. These work in any MCP-compatible client.

What You WantPrompt to Type
Full analysisUse the analyze tool with task: "Should I build feature X or Y?"
Risk checkUse the check_governance tool with task: "Deploy to production on Friday"
Search past decisionsUse the query_intelligence tool with query: "pricing strategy"
Trust assessmentUse the trust_lookup tool for entity: "Stripe" with context: "payment processing"
Log an outcomeUse the report_outcome tool with task: "Raised prices 30%" and outcome: "Revenue up 22%"
List all toolsWhat Boardroom MCP tools do I have? List all 5 with descriptions.
Test installationUse the analyze tool with task: "Test β€” is the Boardroom working?"
πŸ”

Troubleshooting

FREE
πŸ”΄

The AI ignores my prompt and doesn't use the tool

This is the #1 issue β€” it means the MCP server isn't loaded.

  1. Did you restart? Every platform requires a restart after editing the config.
  2. Is the config in the right file? Double-check the file path for your platform.
  3. Is the JSON valid? No trailing commas, no comments. Use jsonlint.com to validate.
  4. Is Node.js 18+ installed? Run node --version in your terminal.
  5. VS Code users: You need "servers" not "mcpServers" β€” VS Code uses a different format.
🟑

"boardroom-mcp not found" or npx fails

The npm package isn't accessible.

  1. Run: npm view boardroom-mcp version β€” to verify the package exists.
  2. Clear cache: npx clear-npx-cache && npx -y boardroom-mcp
  3. Nuclear option: npm install -g boardroom-mcp β€” then use "boardroom-mcp" as the command.
  4. If all else fails: clone the repo and build from source (see Alternative Installation).
🟑

"No advisors found" in the output

The package installed but can't find the demo council files.

  1. Check if demo/seats.md exists in the installed package.
  2. Reinstall: npm install -g boardroom-mcp
  3. If using BOARDROOM_ROOT, ensure the path exists and contains protocol files.
πŸ”΅

ENOENT errors

You're pointing at a BOARDROOM_ROOT directory that doesn't exist.

  1. Check your path: echo $BOARDROOM_ROOT
  2. Unset the variable to fall back to demo: unset BOARDROOM_ROOT
  3. Or create the directory the variable points to.
🟑

Claude Code: "MCP server failed to start"

The MCP registration is corrupted or outdated.

  1. Remove and re-add: claude mcp remove boardroom
  2. Then: claude mcp add boardroom -- npx -y boardroom-mcp
  3. Verify: claude mcp list β€” should show "boardroom" in the list.
πŸ”΅

Permission errors on macOS

npx can't write to the npm cache directory.

  1. Fix ownership: sudo chown -R $(whoami) ~/.npm
  2. Clear cache: npm cache clean --force
  3. Try again: npx -y boardroom-mcp

Platform Config Cheat Sheet

PlatformConfig FileKey NameRestart Method
Claude Desktopclaude_desktop_config.jsonmcpServersQuit + reopen app
Claude Code.mcp.json or claude mcp addmcpServersAuto-reloads
Cursor.cursor/mcp.json or Settings β†’ MCPmcpServersRestart Cursor
Windsurf.windsurf/mcp.json or Settings β†’ MCPmcpServersRestart Windsurf
VS Code.vscode/mcp.json⚠️ serversReload Window
ChatGPT DesktopSettings β†’ Developer β†’ ConnectorsmcpServersToggle connector
Codex CLI~/.codex/config.toml or codex mcp addmcp_servers (TOML)Auto-reloads
Antigravity.mcp.jsonmcpServersAuto-reloads
OpenClaw~/.openclaw/skills/boardroom/SKILL.mdSkills systemRestart gateway
πŸ“‚

Protocol Files

CORE

The MCP server is the engine. Protocol files are the fuel. They define who your advisors are, how they debate, and what your boardroom remembers.

Directory Structure

.ai/boardroom/
.ai/boardroom/
β”œβ”€β”€ LEDGER.md                    # Decision log (institutional memory)
β”œβ”€β”€ BOARD_WISDOM.md              # Distilled principles from past sessions
β”œβ”€β”€ mastermind/
β”‚   β”œβ”€β”€ SYSTEM_PROMPT.md         # Core operating rules for the boardroom
β”‚   β”œβ”€β”€ RULE_MATRIX.md           # Which rules fire at each severity
β”‚   β”œβ”€β”€ COGNITIVE_DOSSIERS.md    # Deep profiles for each advisor
β”‚   β”œβ”€β”€ SIGNATURE_QUESTIONS.md   # Each advisor's unique probing questions
β”‚   β”œβ”€β”€ CONVERSATION_TEMPLATE.md # How debate sessions are structured
β”‚   β”œβ”€β”€ seats/                   # Individual advisor definitions
β”‚   β”‚   β”œβ”€β”€ 01-ceo.md
β”‚   β”‚   β”œβ”€β”€ 02-product.md
β”‚   β”‚   β”œβ”€β”€ 03-marketing.md
β”‚   β”‚   └── ...
β”‚   └── councils/                # Specialized expert panels
β”‚       β”œβ”€β”€ tech/
β”‚       β”œβ”€β”€ survival/
β”‚       β”œβ”€β”€ legal/
β”‚       └── ...

How the server finds them

The MCP server looks for protocol files in this order:

  1. BOARDROOM_ROOT environment variable (if set)
  2. SALARSNET_ROOT/.ai/boardroom (if set)
  3. $HOME/Projects/salarsu/.ai/boardroom (default)
  4. Falls back to built-in demo council (Buffett, Torvalds, Aurelius)

πŸ’‘ Want the full protocol files?

The open-source repo includes a demo council. The full system with 450+ calibrated advisors, 38 councils, cognitive dossiers, and debate protocols is available at salars.net/boardroom.

πŸ›οΈ

Building Your Own Councils

CORE

You don't have to use pre-built advisors. Create your own councils tailored to your domain.

Anatomy of a Seat Card

Each advisor is defined by a seat card β€” a markdown file that describes their personality, expertise, decision criteria, and communication style.

seats/custom-advisor.md
# Seat: Chief Security Officer

## Role
Security & Risk Assessment Lead

## Core Philosophy
"Every system is as secure as its weakest link. 
 Assume breach. Design for resilience."

## Decision Criteria
1. Does this introduce new attack surface?
2. Does this follow the principle of least privilege?
3. Is there a rollback plan if this is compromised?
4. Have we considered supply chain risks?

## Veto Power
Can VETO any action that:
- Exposes PII without encryption
- Removes authentication from an endpoint
- Introduces unaudited third-party dependencies

## Signature Questions
- "What happens if this is compromised at 3 AM?"
- "Who has access, and should they?"
- "What does the blast radius look like?"

## Advice Style
Direct. Data-driven. Uses threat modeling frameworks.
Never says "it depends" without specifying conditions.

Creating a Council

Group related advisors into a council folder:

Directory structure
.ai/boardroom/mastermind/councils/security/
β”œβ”€β”€ council-config.md        # Council metadata + routing rules
β”œβ”€β”€ seats/
β”‚   β”œβ”€β”€ cso.md              # Chief Security Officer
β”‚   β”œβ”€β”€ red-team.md          # Red Team Lead
β”‚   β”œβ”€β”€ compliance.md        # Compliance Expert
β”‚   └── forensics.md         # Digital Forensics Analyst
councils/security/council-config.md
# Security & Risk Council

## Routing Keywords
security, authentication, authorization, encryption, 
vulnerability, compliance, GDPR, SOC2, penetration, audit

## Default Severity
standard (elevated to critical for auth/encryption topics)

## Members
- CSO (Chair)
- Red Team Lead
- Compliance Expert
- Digital Forensics Analyst

## Resolution Protocol
Unanimous on security vetoes. Majority on recommendations.
βš”οΈ

Debate Protocols

ADVANCED

Real boardrooms don't just give opinions β€” they argue until something true emerges. The Boardroom MCP supports 5 resolution types for structured intellectual combat.

Consensus

All advisors converge on a shared position. Used when alignment matters more than speed.

Best for: Values decisions, mission-critical choices

Majority Vote

Simple majority carries. Dissenting opinions are preserved and logged.

Best for: Tactical decisions, time-sensitive choices

Dialectic Synthesis

Thesis β†’ Antithesis β†’ Synthesis. Forces opposing views to produce a new, higher-order insight.

Best for: Complex strategy, paradoxical situations

Adversarial

No resolution sought. Raw tension preserved as data. Used when disagreement IS the insight.

Best for: Risk assessment, stress testing assumptions

Omega Synthesis

CEO/Integrator synthesizes all positions into a final decision with explicit tradeoffs named.

Best for: Default for standard/critical severity sessions

Conversation Mode (Rule 12)

For Standard (🟑) and Critical (πŸ”΄) sessions, advisors don't give monologues β€” they debate each other. The conversation follows this structure:

Conversation Structure
1. Opening Positions
   Each active advisor gives a raw 2-3 sentence gut reaction

2. Interactive Dialogue
   Advisors respond to EACH OTHER:
   **[Member A]**: "Here's why I disagree with B's position..."
   **[Member B]**: "That ignores the data showing..."
   **[Member A] shifts position**: "Actually, if we combine..."

3. Emergent Insights
   Ideas that ONLY emerged from the interaction itself

4. Unresolved Tensions
   Disagreements preserved as data β€” not forced into consensus
🧠

Cognitive Drills

ADVANCED

10 cognitive exercises to sharpen your boardroom's thinking. Use Board: Drill [#] to invoke any drill.

#1

Pre-Mortem

Assume the project failed. Why?

#2

Inversion

How would we guarantee failure?

#3

Steel Man

Make the strongest case for the opposing view

#4

Red Team

Attack your own plan as an adversary would

#5

Second-Order

What are the consequences of the consequences?

#6

Regret Minimization

Which choice minimizes regret at age 80?

#7

Opportunity Cost

What are we NOT doing by choosing this?

#8

Boundary Push

What if we 10x'd this? What if we did 1/10th?

#9

Historical Parallel

When has a similar situation played out before?

#10

Assumption Audit

List every assumption. Test each one.

⏳

Mind Versioning & Prometheus Protocol

ADVANCED

Mind Versioning

Invoke advisors at different points in their lives. Young Steve Jobs (1984, hungry rebel) gives different advice than Late-Stage Jobs (2010, product philosopher).

Mind Versioning example
"Board: I want to hear from Young Buffett (1960s, 
aggressive value hunter) AND Late Buffett (2020s, 
quality compounder) on this acquisition."

// The engine invokes two versions of the same advisor
// with different calibration profiles, creating temporal
// debate between past-self and future-self.

The Prometheus Protocol πŸ”₯

When no existing council covers a topic, forge a new domain on the fly. The Prometheus Protocol dynamically creates temporary advisors calibrated for the specific question.

Prometheus example
"Board: I need expertise on quantum error correction 
for my startup's hardware. None of your councils 
cover quantum computing."

// Prometheus Protocol activates:
// 1. Identifies knowledge gap
// 2. Forges 3-5 temporary advisors calibrated for the domain
// 3. Runs a single-session debate
// 4. Advisor profiles are NOT persisted (fire-and-forget)

Meta-Observer πŸ‘οΈ

A silent advisor that watches every session for cognitive bias. It flags:

  • Groupthink (all advisors converging too quickly)
  • Anchoring (first speaker disproportionately influences the group)
  • Confirmation bias (ignoring disconfirming evidence)
  • Availability bias (overweighting recent/memorable events)
  • Sunk cost fallacy (justifying past investments)
πŸ—οΈ

Architecture

REFERENCE

The Boardroom MCP follows a local-first, zero-dependency architecture.

Architecture Overview
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚               Your AI Client                     β”‚
β”‚     (Claude, Cursor, Windsurf, OpenClaw, etc.)   β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                  β”‚ MCP Protocol (STDIO)
                  β”‚ (runs 100% on YOUR machine)
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚           Boardroom MCP Server                   β”‚
β”‚  β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚
β”‚  β”‚ analyze  β”‚governanceβ”‚  intel   β”‚   trust   β”‚ β”‚
β”‚  β”‚          β”‚          β”‚          β”‚           β”‚ β”‚
β”‚  β””β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”˜ β”‚
β”‚       β”‚          β”‚          β”‚          β”‚        β”‚
β”‚  β”Œβ”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β” β”‚
β”‚  β”‚           File System Reader               β”‚ β”‚
β”‚  β”‚    (reads .ai/boardroom/ directory)        β”‚ β”‚
β”‚  β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                  β”‚
      β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
      β”‚  .ai/boardroom/        β”‚
      β”‚  β”œβ”€β”€ LEDGER.md         β”‚  ← Institutional memory
      β”‚  β”œβ”€β”€ BOARD_WISDOM.md   β”‚  ← Distilled principles
      β”‚  └── mastermind/       β”‚  ← Advisor definitions
      β”‚      β”œβ”€β”€ seats/        β”‚
      β”‚      β”œβ”€β”€ councils/     β”‚
      β”‚      └── protocols/    β”‚
      β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

Key Properties

Local-First

Everything runs on your machine. No network calls. No API keys.

Zero Cost

No hosting. No bandwidth. Your AI client does the LLM processing.

File-Based

Protocol files are markdown. Edit with any text editor.

Composable

Mix pre-built councils with your own custom advisors.

πŸ”—

OpenClaw Deep Integration

INTEGRATION

The Boardroom MCP plugs directly into OpenClaw's autonomous agent infrastructure β€” 10 deep integration points that turn every cron job, skill, and safety gate into a boardroom-aware decision node.

πŸ›οΈ

Boardroom Sessions

MCP-first analysis with named advisors, automatic fallback to generic 7-seat simulation.

πŸ›‘οΈ

Governance Gate

Pre-execution risk check for cron jobs. Routine tasks auto-approve; critical ones pause for human review.

πŸ’‘

AUREUS Idea Stress-Test

High-impact monetization ideas are routed through boardroom analysis before execution.

🀝

A2A Trust Oracle

Cross-references trust profiles against the boardroom's 6-dimension trust vector.

🚨

Sentinel Watcher Hook

Anomalies from Sentinel auto-route through boardroom governance for risk classification.

πŸ“‹

Decision Queue

Queues boardroom decisions during heartbeat cycles. Routine actions auto-approve; critical ones wait.

πŸ“š

Course β†’ Board Routing

Course analysis uses MCP named advisors first, falls back to Gemini API if unavailable.

πŸ“Š

Decision Queue Processing

Batch processes pending decisions with configurable auto-approve thresholds.

πŸ”΄

Kill Switch Governance

Freeze events route through boardroom for analysis and outcome logging.

🧭

Narrative Guard

When brand drift score drops below 60, Values & Alignment Council provides correction recommendations.

How It Works

Every integration follows the same resilient pattern:

1. Try MCP First

Named advisors, protocol files, calibrated philosophies

2. Graceful Fallback

If MCP unavailable, existing behavior continues uninterrupted

3. Learn & Report

Outcomes logged to boardroom memory for continuous learning

OpenClaw Skill Installation
# 1. Clone the protocol files
git clone https://github.com/randysalars/boardroom-mcp.git
cp -r boardroom-mcp/protocol-files ~/.ai/boardroom

# 2. Install the MCP server
cd boardroom-mcp && npm install && npm run build

# 3. Register as OpenClaw skill
mkdir -p ~/.openclaw/skills/boardroom
cat > ~/.openclaw/skills/boardroom/SKILL.md << 'EOF'
---
name: boardroom
description: Boardroom Mastermind Council analysis
---
EOF

# 4. Set environment variable
echo 'BOARDROOM_ROOT=~/.ai/boardroom' >> ~/.openclaw/.env

# 5. Register the skill
openclaw skills register boardroom --always
πŸ‘‘

The Full System

PRO

The open-source MCP server is the engine. The full Boardroom system is the engine plus 2 years of hand-calibrated intelligence.

What's included in the full system

βœ“450+ named advisors with calibrated seat cards
βœ“38 expert councils (Tech, Business, Survival, Legal...)
βœ“Cognitive dossiers for every advisor
βœ“Signature questions per advisor
βœ“69+ LEDGER decisions as precedent library
βœ“113+ Wisdom Codex entries
βœ“10 cognitive drills
βœ“35+ session modes (Shadow, Crisis, Launch...)
βœ“Mind Versioning profiles
βœ“Prometheus Protocol for dynamic domains
βœ“Meta-Observer bias detection
βœ“Debate protocol templates
βœ“Smart Router (auto-detects council + severity)
βœ“DQ Scorecard for session quality tracking
βœ“Calibration engine for advisor accuracy
βœ“Context injection with live metrics