NerdCabalMCP

🧪 Testing Guide for NerdCabalMCP

This guide provides comprehensive instructions for testing the NerdCabalMCP server and all 17 specialized agents.

📋 Table of Contents

🔧 Prerequisites

Before testing, ensure you have:

  1. Node.js 18+ installed 
    node --version  # Should be v18.x.x or higher
  2. Built the MCP server 
    cd mcp-server
npm install
npm run build
  3. Claude Desktop installed (for MCP client testing)
    • Download from: https://claude.ai/download
  4. Configured Claude Desktop with the MCP server

🚀 Quick Start Testing

Method 1: Standalone Server Test

Test the server runs without errors:

cd mcp-server
node dist/index.js

Expected output:

MCP server running on stdio

If you see this, the server is working! Press Ctrl+C to stop.

Method 2: Health Check Test

Test the server responds to basic requests:

cd mcp-server
echo '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0.0"}}}' | node dist/index.js

Expected: JSON response with server capabilities

Method 3: Claude Desktop Test

  1. Restart Claude Desktop (completely quit and reopen)
  2. Open a new chat
  3. Type @ to see available tools
  4. Look for nerdcabal tools

If you see tools like llm-rubric-architect, experimental-designer, etc., the MCP is working!

🤖 Testing Individual Agents

1. LLM Rubric Architect

Purpose: Creates evaluation rubrics for AI systems

Test Prompt in Claude Desktop:

@nerdcabal Use llm-rubric-architect to create a rubric for evaluating chatbot responses.
Include these dimensions: accuracy, helpfulness, safety, and tone. Use a 1-5 scale.

Expected Output:

2. Experimental Designer

Purpose: Designs controlled experiments

Test Prompt:

@nerdcabal Use experimental-designer to design an A/B test comparing two prompting strategies.
Hypothesis: Chain-of-thought prompting improves accuracy on math problems.
Baseline: Direct answer prompts
Intervention: Chain-of-thought prompts
Metric: Accuracy
Sample size: 1000

Expected Output:

3. Budget Agent

Purpose: Creates financial budgets and projections

Test Prompt:

@nerdcabal Use budget-agent to create a budget for an AI research project.
Project: LLM fine-tuning research
Funding target: $500,000
Timeline: 18 months
Categories: personnel, compute, equipment
Format: NSF

Expected Output:

4. Comptroller Agent

Purpose: Operations management and Iron Triangle optimization

Test Prompt:

@nerdcabal Use comptroller-agent to analyze the trade-offs for a project.
Project: Build a new feature for our app
Timeline: 2 weeks
Budget: $10,000
Quality requirements: Production-ready, full test coverage

Expected Output:

5. Administrator Agent

Purpose: Organizational design and SOPs

Test Prompt:

@nerdcabal Use administrator-agent to design an org chart for a distributed AI team.
Team size: 15 people
Timezones: US East, US West, EU, Asia
Roles needed: engineers, researchers, designers, product managers

Expected Output:

6. MLflow Agent

Purpose: Experiment tracking queries

Test Prompt:

@nerdcabal Use mlflow-agent to generate a query that finds the top 10 model runs
with the highest accuracy from the last 30 days.

Expected Output:

Note: Requires MLflow server running for full functionality

7. Dataset Builder

Purpose: Creates training datasets for ML

Test Prompt:

@nerdcabal Use dataset-builder to create a supervised fine-tuning (SFT) dataset
for teaching a model to write Python code.
Include 5 examples with prompts and completions.
Output format: HuggingFace compatible

Expected Output:

8. CISO Agent

Purpose: Security threat modeling

Test Prompt:

@nerdcabal Use ciso-agent to perform a STRIDE threat model for an LLM API.
Components: API gateway, model inference server, user database
Framework: STRIDE

Expected Output:

9. Orchestrator

Purpose: Multi-agent workflow coordination

Test Prompt:

@nerdcabal Use orchestrator to create a sequential workflow:
1. Use experimental-designer to create an experiment plan
2. Use budget-agent to budget the experiment
3. Use administrator-agent to staff the team

Expected Output:

10. Creative Director

Purpose: Design systems and UI/UX

Test Prompt:

@nerdcabal Use creative-director to create a design system.
Style: cyberpunk-brutalist-bauhaus
Colors: black, white, red
Components: buttons, cards, navigation
Output format: CSS

Expected Output:

11. Visual Inspector

Purpose: Dataset visualization and quality analysis

Test Prompt:

@nerdcabal Use visual-inspector to generate a FiftyOne visualization script
for analyzing an image classification dataset.
Dataset: CIFAR-10
Tasks: Find mislabeled images, detect outliers

Expected Output:

Note: Requires FiftyOne installed for execution

12. Forensic Analyst

Purpose: Neural forensics for LLM analysis

Test Prompt:

@nerdcabal Use forensic-analyst to analyze this transcript for hallucinations:

"User: What's the capital of France?
Assistant: The capital of France is Paris, which was founded in 1850 by Napoleon Bonaparte
and has a population of 50 million people."

Use DSMMD taxonomy to detect confabulation, metadata leakage, or semantic drift.

Expected Output:

13-14. Enterprise IP Protection Suite

IP Analytics Engine

Test Prompt:

@nerdcabal Use ip_analytics to analyze copyright infringement patterns.
IP type: copyright
Timeframe: last 90 days
Portfolio IDs: PORT-001, PORT-002
Jurisdiction: US

Expected Output:

Compliance Engine

Test Prompt:

@nerdcabal Use compliance_check to validate GDPR compliance.
Context:
- Processes personal data: yes
- Consent obtained: yes
- Data retention policy: 2 years
- Right to deletion: implemented
Jurisdiction: EU

Expected Output:

Archival System

Test Prompt:

@nerdcabal Use archival_system to store evidence of IP infringement.
Evidence type: image
Source URL: https://example.com/infringement.jpg
Description: Unauthorized use of copyrighted work
Jurisdiction: US
Case ID: CASE-2026-001

Expected Output:

🔗 Integration Testing

Test 1: Multi-Agent Workflow

Scenario: Complete research project planning

Steps:

  1. Use experimental-designer to create experiment plan
  2. Use budget-agent to create budget
  3. Use administrator-agent to design team structure
  4. Use ciso-agent to assess security risks

Prompt:

@nerdcabal Let's plan a research project step by step:

1. First, use experimental-designer to design an experiment for testing a new prompting technique
2. Then use budget-agent to create a 6-month budget for $200k
3. Then use administrator-agent to design a team of 5 people
4. Finally, use ciso-agent to identify security risks

Test 2: Creative + Technical Workflow

Scenario: Design and validate a UI component

Steps:

  1. Use creative-director to create design system
  2. Use ciso-agent to review for security issues
  3. Use dataset-builder to create training data for UI testing

Test 3: IP Protection Workflow

Scenario: Detect, validate, and archive infringement

Steps:

  1. Use ip_analytics to detect infringement patterns
  2. Use compliance_check to validate enforcement actions
  3. Use archival_system to store evidence

🐛 Troubleshooting

Issue: Tools not appearing in Claude Desktop

Solutions:

  1. Verify claude_desktop_config.json has correct absolute path
  2. Check the path in config matches your actual file location
  3. Completely restart Claude Desktop (quit entirely, not just close window)
  4. Check logs: 
    # macOS
cat ~/Library/Logs/Claude/mcp*.log

# Windows
type %APPDATA%\Claude\Logs\mcp*.log

# Linux
cat ~/.config/Claude/logs/mcp*.log

Issue: Server not responding

Solutions:

  1. Test server runs standalone: 
    cd mcp-server
node dist/index.js
  2. Check for errors in terminal output
  3. Rebuild the server: 
    npm run build

Issue: Agent returns error

Solutions:

  1. Verify input matches the agent’s required schema (see README.md)
  2. Check you’re using the correct tool name (e.g., llm-rubric-architect not rubric-architect)
  3. Enable debug logging: 
    LOG_LEVEL=debug node dist/index.js

Issue: External integrations failing

MLflow:

# Start MLflow server
mlflow server --host 0.0.0.0 --port 5000

# Set environment variable
export MLFLOW_TRACKING_URI=http://localhost:5000

FiftyOne:

# Install FiftyOne
pip install fiftyone

# Start FiftyOne app
fiftyone app launch

HuggingFace:

# Login to HuggingFace
huggingface-cli login

# Verify
huggingface-cli whoami

📊 Test Scenarios

Scenario 1: Academic Researcher

Goal: Plan a research project for a grant proposal

Agents to use:

  1. experimental-designer - Design the experiment
  2. budget-agent - Create NSF grant budget
  3. dataset-builder - Plan training data
  4. mlflow-agent - Setup experiment tracking

Scenario 2: Security Auditor

Goal: Audit an AI system for security issues

Agents to use:

  1. ciso-agent - STRIDE threat model
  2. forensic-analyst - Analyze outputs for issues
  3. ip_analytics - Check for IP compliance
  4. compliance_check - Validate regulatory compliance

Scenario 3: Product Designer

Goal: Design and launch a new feature

Agents to use:

  1. creative-director - Create design system
  2. comptroller-agent - Analyze speed/cost/quality tradeoffs
  3. administrator-agent - Design team structure
  4. ciso-agent - Security review

Scenario 4: Enterprise IP Manager

Goal: Monitor and protect intellectual property

Agents to use:

  1. ip_analytics - Detect infringement patterns
  2. compliance_check - Validate enforcement actions
  3. archival_system - Store evidence
  4. ciso-agent - Security audit

✅ Verification Checklist

Use this checklist to verify all agents are working:

📚 Additional Resources

🤝 Getting Help

Happy Testing! 🧪

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