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🏛️ Boardroom MCP

AI Governance-as-a-Service — Model Context Protocol Server

MIT License npm MCP

Give your AI agents a boardroom of advisors. Based on Napoleon Hill's Mastermind Principle — the idea that coordinated minds produce intelligence no single mind can achieve — digitized for AI agents.

Multi-advisor debate, institutional memory, trust scoring, and cognitive governance — all running locally on your machine.

📖 Read the Full Documentation →

Complete guide covering Quick Start → Installation → 5 Tools → Use Cases → Protocol Files → Building Councils → Debate Protocols → Cognitive Drills → Mind Versioning → Architecture → Full System.


Related MCP server: deep-thinking-engine

⚡ Quick Start — Pick Your Platform

Prerequisites: Node.js 18+ must be installed. Check with node --version.


🟣 Claude Desktop

Step 1: Open your config file:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

  • Windows: %APPDATA%\Claude\claude_desktop_config.json

Step 2: Paste this (create the file if it doesn't exist):

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

Step 3: Restart Claude Desktop completely (quit and reopen).

Step 4: Test it — type this in the chat:

Use the analyze tool with task: "Test — is the Boardroom working?"

Step 5: See what's available — type this in the chat:

What Boardroom MCP tools do I have access to? List all 5 tools with a one-line description of each.

🟢 Claude Code (CLI)

Option A — One command (recommended):

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

Option B — Config file: Create .mcp.json in your project root:

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

Test it — type this in Claude Code:

Use the analyze tool with task: "Test — is the Boardroom working?"

See what's available:

What Boardroom MCP tools do I have access to? List all 5 tools with a one-line description of each.

🔵 Cursor

Step 1: Open Settings → MCP (or create .cursor/mcp.json in your project root)

Step 2: Add this server config:

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

Step 3: Restart Cursor.

Test it — type in Cursor chat:

Use the analyze tool with task: "Test — is the Boardroom working?"

See what's available:

What Boardroom MCP tools do I have access to? List all 5 tools with a one-line description of each.

🟡 Windsurf

Step 1: Open Settings → MCP or create .windsurf/mcp.json in your project root.

Step 2: Add this server config:

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

Step 3: Restart Windsurf.

Test it — type in Windsurf chat:

Use the analyze tool with task: "Test — is the Boardroom working?"

See what's available:

What Boardroom MCP tools do I have access to? List all 5 tools with a one-line description of each.

🔷 VS Code (GitHub Copilot)

Requires GitHub Copilot with agent mode enabled.

Step 1: Enable MCP: Settings → Copilot → MCP (toggle on).

Step 2: Create .vscode/mcp.json in your project root:

{
  "servers": {
    "boardroom": {
      "command": "npx",
      "args": ["-y", "boardroom-mcp"]
    }
  }
}

⚠️ Note: VS Code uses "servers" — NOT "mcpServers". This is different from all other platforms.

Step 3: Reload VS Code window (Ctrl+Shift+P → "Reload Window").

Test it — type in Copilot Chat (Agent mode):

Use the analyze tool with task: "Test — is the Boardroom working?"

See what's available:

What Boardroom MCP tools do I have access to? List all 5 tools with a one-line description of each.

⚫ ChatGPT Desktop

Requires ChatGPT Plus or Pro subscription.

Step 1: Open ChatGPT Desktop → Settings → Developer → Connectors.

Step 2: Click "Add Custom MCP Server" and paste:

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

Step 3: Enable the "boardroom" connector in your chat.

Test it — type in the chat:

Use the analyze tool with task: "Test — is the Boardroom working?"

See what's available:

What Boardroom MCP tools do I have access to? List all 5 tools with a one-line description of each.

🟠 OpenAI Codex CLI

Option A — One command (recommended):

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

Option B — Config file: Add to ~/.codex/config.toml:

[mcp_servers.boardroom]
type = "stdio"
command = "npx"
args = ["-y", "boardroom-mcp"]

Test it — type in Codex CLI:

Use the analyze tool with task: "Test — is the Boardroom working?"

See what's available:

What Boardroom MCP tools do I have access to? List all 5 tools with a one-line description of each.

🔴 Antigravity

Create .mcp.json in your workspace root:

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

Test it — type in the chat:

Use the analyze tool with task: "Test — is the Boardroom working?"

See what's available:

What Boardroom MCP tools do I have access to? List all 5 tools with a one-line description of each.

🦞 OpenClaw

OpenClaw uses its skills system instead of MCP server configs.

Step 1: Clone and build the server:

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

Step 2: Create the skill directory:

mkdir -p ~/.openclaw/skills/boardroom

Step 3: Create ~/.openclaw/skills/boardroom/SKILL.md:

---
name: boardroom
description: Boardroom Mastermind Council — multi-advisor strategic analysis via MCP server.
metadata: {"clawdbot":{"emoji":"🏛️","always":true,"requires":{"bins":["node"]},"primaryEnv":"BOARDROOM_ROOT"}}
---

# Boardroom MCP 🏛️

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

Step 5: Restart and verify:

systemctl --user restart openclaw-gateway.service
openclaw skills list | grep boardroom
# ✓ ready │ 📦 boardroom

Test it — type in the chat:

Use the analyze tool with task: "Test — is the Boardroom working?"

See what's available:

What Boardroom MCP tools do I have access to? List all 5 tools with a one-line description of each.

✅ What Success Looks Like

When the test prompt works, you'll see output like:

# Boardroom Analysis

## Advisors Consulted
- **Warren Buffett** (Business Strategy): [their position]
- **Linus Torvalds** (Technology): [their position]
- **Marcus Aurelius** (Values & Ethics): [their position]

## Verdict
[synthesized recommendation]

## Recommended Actions
1. [action item]
2. [action item]

If you see this, it's working. Try a real question next:

Use the analyze tool with task: "Should I raise my SaaS price from $29 to $49?"

📖 Quick Command Reference

Copy-paste these prompts into your AI chat to use each tool:

What You Want

Prompt to Type

Full analysis

Use the analyze tool with task: "Should I build feature X or Y?"

Risk check

Use the check_governance tool with task: "Deploy to production on Friday"

Search past decisions

Use the query_intelligence tool with query: "pricing strategy"

Trust assessment

Use the trust_lookup tool for entity: "Stripe" with context: "payment processing"

Log an outcome

Use the report_outcome tool with decision: "Raised prices 30%" and outcome: "Revenue up 22%"

List all tools

What Boardroom MCP tools do I have? List all 5 with descriptions.


📦 Alternative Installation Methods

# Option A: npx (used by MCP configs above — no global install needed)
npx -y boardroom-mcp

# Option B: Global install
npm install -g boardroom-mcp
boardroom-mcp

# Option C: Clone and build (for development/contributing)
git clone https://github.com/randysalars/boardroom-mcp.git
cd boardroom-mcp && npm install && npm run build

Note: If npx fails, use Option C (clone and build), then point your MCP config to the local build:

{
  "mcpServers": {
    "boardroom": {
      "command": "node",
      "args": ["/path/to/boardroom-mcp/dist/index.js"]
    }
  }
}

🔧 5 MCP Tools

Tool

Purpose

analyze

Full boardroom consultation with multi-advisor debate

check_governance

Task classification + severity routing

query_intelligence

Search LEDGER decisions + Wisdom Codex

trust_lookup

6-dimension trust vector for any entity

report_outcome

Log outcomes for institutional memory

→ See detailed tool documentation with examples


🏗️ Architecture

Your AI Client (Claude, Cursor, Windsurf, VS Code, ChatGPT, Codex, Antigravity, OpenClaw)
         │ MCP Protocol (STDIO)
         │ Runs 100% on YOUR machine
         ▼
  Boardroom MCP Server
         │
         ├── demo/              ← Demo council (Buffett, Torvalds, Aurelius)
         │
         ▼
  ~/.ai/boardroom/          ← Full protocol files (optional upgrade)
  ├── LEDGER.md              ← Institutional memory
  ├── BOARD_WISDOM.md        ← Distilled principles
  └── mastermind/
      ├── seats/             ← Advisor definitions
      ├── councils/          ← Expert panels
      └── protocols/         ← Debate rules

Zero cost. No API keys. No cloud. No hosting. Your AI client does the LLM processing.


🎯 What You Get

Free (This Repo)

  • ✅ MCP server with 5 tools

  • ✅ Demo council (3 named advisors: Warren Buffett, Linus Torvalds, Marcus Aurelius)

  • ✅ MIT license

Full System (salars.net/boardroom)

  • 🏛️ 450+ named advisors with calibrated seat cards (Buffett, Torvalds, Aurelius...)

  • 🏛️ 38 expert councils (Tech, Business, Survival, Legal, Creative...)

  • ⚔️ 5 debate resolution types

  • 🧠 10 cognitive drills

  • ⏳ Mind Versioning (Young Jobs vs Late Jobs)

  • 🔥 Prometheus Protocol (forge new domains on the fly)

  • 👁️ Meta-Observer (real-time bias detection)

  • 📚 69+ LEDGER decisions as precedent library

  • 📖 113+ Wisdom Codex entries

  • 🎯 Smart Router (auto-detects council + severity)


⚙️ Environment Variables

Variable

Default

Description

BOARDROOM_ROOT

~/.ai/boardroom

Path to your full protocol files directory

BOARDROOM_TRUST_PATH

~/.boardroom/trust-oracle.json

Path to trust oracle data file


🔍 Troubleshooting

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

This is the #1 issue. It means the MCP server isn't loaded. Fix it:

  1. Did you restart? Every platform requires a restart after editing the config. Quit completely and reopen.

  2. Is the config in the right file? Double-check the file path for your platform (see Quick Start above).

  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. Must be 18.0.0 or higher.

  5. VS Code users: You need "servers" not "mcpServers" — VS Code uses a different format.

The AI says "boardroom-mcp not found" or npx fails

# Verify the package exists
npm view boardroom-mcp version

# If that works but npx doesn't, clear cache:
npx clear-npx-cache
npx -y boardroom-mcp

# Nuclear option — install globally:
npm install -g boardroom-mcp

Then update your config to use the global install:

{
  "mcpServers": {
    "boardroom": {
      "command": "boardroom-mcp"
    }
  }
}

"No advisors found" in the output

The demo council file isn't being found. This means the package installed but can't find demo/seats.md.

# Check if the demo file exists in the package
npx -y boardroom-mcp --help 2>/dev/null
ls $(npm root -g)/boardroom-mcp/demo/

If the demo directory is missing, reinstall: npm install -g boardroom-mcp

ENOENT errors

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

# Check what path it's looking for
echo $BOARDROOM_ROOT

# Create it or unset the variable:
unset BOARDROOM_ROOT  # falls back to demo council

Tools appear but return errors

# Test the server directly in your terminal:
npx -y boardroom-mcp

# If it starts without errors, the MCP server works.
# The issue is in your AI client's connection to it.
# Try removing and re-adding the MCP config.

Claude Code specific: "MCP server failed to start"

# Remove and re-add:
claude mcp remove boardroom
claude mcp add boardroom -- npx -y boardroom-mcp

# Verify it's registered:
claude mcp list

Permission errors on macOS

# If npx can't write to the cache:
sudo chown -R $(whoami) ~/.npm
npm cache clean --force

Platform-specific config cheat sheet

Platform

Config File

Key Name

Restart Method

Claude Desktop

claude_desktop_config.json

mcpServers

Quit + reopen app

Claude Code

.mcp.json or claude mcp add

mcpServers

Auto-reloads

Cursor

.cursor/mcp.json or Settings → MCP

mcpServers

Restart Cursor

Windsurf

.windsurf/mcp.json or Settings → MCP

mcpServers

Restart Windsurf

VS Code

.vscode/mcp.json

⚠️ servers

Reload Window

ChatGPT Desktop

Settings → Developer → Connectors

mcpServers

Toggle connector

Codex CLI

~/.codex/config.toml or codex mcp add

mcp_servers (TOML)

Auto-reloads

Antigravity

.mcp.json

mcpServers

Auto-reloads

OpenClaw

~/.openclaw/skills/boardroom/SKILL.md

Skills system

Restart gateway


📬 Stay in the Loop


🤝 Contributing

PRs welcome! Areas we'd love help with:

  • Additional demo advisors

  • New cognitive drill templates

  • Documentation improvements

  • Bug fixes


📄 License

MIT — Built by SalarsNet

Available Tools

5 tools
analyzeA

Run a Boardroom consultation. Routes your question to relevant advisors, loads their philosophies and decision criteria, searches institutional memory for precedents, and provides a structured analysis with mandatory tension between opposing viewpoints. Demo mode includes 3 named advisors; full protocol files unlock 450+ advisors across 38 councils.

Inputs: task (required).

check_governanceA

Classify a task and determine which governance advisors should review it. Returns the decision type, selected advisors, risk level, and whether constitutional constraints apply. Fast classification without running the full session.

Inputs: task (required).

query_intelligenceA

Search the Boardroom LEDGER (persistent decision memory) and Wisdom Codex for relevant precedents, past decisions, and distilled insights. Returns keyword-matched results with timestamps and excerpts. The LEDGER grows each time you use report_outcome.

Inputs: query (required), limit.

report_outcomeA

Report the outcome of a decision for the Boardroom learning system. Records what happened, whether the original recommendation was followed, and what was learned. Feeds the Knowledge Flywheel and updates the Trust Oracle if an entity is specified. Returns a warning if the outcome could not be persisted to disk.

Inputs: task (required), outcome (required), entity, followedRecommendation.

trust_lookupA

Look up the trust profile for any entity (AI agent, tool, vendor, platform). Returns a 6-dimension trust vector (reliability, honesty, follow-through, outcome quality, stability, risk profile), composite score, and recommendation (trust/verify/caution/avoid). New entities return a default "unknown" profile — use report_outcome to build trust data over time.

Inputs: entity (required), context.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A3.9/5.0

    Scored across 5 tools

    Disambiguation4/5

    Each tool targets a distinct action: classification, search, trust lookup, outcome reporting, and full consultation. The only minor overlap is between query_intelligence and analyze, as both access institutional memory, but their purposes are clearly differentiated by analysis' routing to advisors.

    Naming Consistency3/5

    Tool names mix verb_noun patterns (check_governance, query_intelligence, report_outcome) with a noun_verb compound (trust_lookup) and a single verb (analyze). This is readable but not fully consistent, making the pattern less predictable than it could be.

    Tool Count5/5

    Five tools is well-scoped for a governance/consultation server, covering classification, memory search, trust evaluation, outcome feedback, and full analysis without redundancy. Each tool earns its place in the workflow.

    Completeness4/5

    The core lifecycle is covered: classify, analyze, query, report, and trust lookup are present, with feedback loops for learning. Minor gaps exist (e.g., no direct tool for editing governance protocols), but agents can work around this with the existing tools.

    Maintenance

    ActivityInactive
    ResponsivenessLimited data

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