Fix test compatibility with FastMCP FunctionTool wrapper

[Copilot]

The @mcp.tool() decorator wraps functions as FunctionTool objects, breaking direct invocation in tests.

Changes

• tests/test_intelligentone_server.py: Unwrap FunctionTool objects via .fn attribute to access underlying async functions
• tests/test_techcrunch_monitor.py: Same unwrapping pattern, excluding non-decorated functions

Implementation

from intelligentone_server import listen_to_rss, extract_ogp_capabilities

# Unwrap FunctionTool objects to get callable functions
if hasattr(listen_to_rss, 'fn'):
    listen_to_rss = listen_to_rss.fn
if hasattr(extract_ogp_capabilities, 'fn'):
    extract_ogp_capabilities = extract_ogp_capabilities.fn

# Now callable in tests
entries = await listen_to_rss("https://example.com/feed", keywords=["AI"])

This pattern handles both decorated (wrapped) and undecorated functions gracefully via runtime inspection.

Result

All 21 tests pass. Zero security vulnerabilities (CodeQL).

Original prompt

---

This section details on the original issue you should resolve

<issue_title>🚀 Replace codebase with self-evolving Omen Intelligence architecture (MCP + LLM + Blueprint Vault + Dashboard)</issue_title>
<issue_description>## Strategic Objective

Replace the current generic "web as executable OS" implementation with our purpose-built Omen Intelligence architecture, renamed as intelligentOne for brand consistency.

Why This Change?

Current State (Generic Capability Engine)

• ❌ No self-evolution or learning capabilities
• ❌ No LLM-powered workflow generation
• ❌ No real-time web signal monitoring (RSS/OGP)
• ❌ Static tool registry
• ❌ Doesn't match our billion-dollar positioning

New State (Omen Intelligence / intelligentOne)

• ✅ Self-evolving Blueprint Vault (tools create tools)
• ✅ LLM-powered hypothesis generation (GPT-4o/Claude)
• ✅ Real-time RSS + OGP metadata extraction
• ✅ Simulation Lab with LLM-as-Judge
• ✅ Hot-reload autonomous deployment
• ✅ Visual dashboard for monitoring evolution
• ✅ MCP-native architecture

Architecture Overview

intelligentOne/
├── intelligentone_server.py      # Main MCP server
├── simulation_lab.py             # LLM-as-Judge execution
├── blueprint_vault.py            # Recipe storage
├── router_agent.py               # Intent classifier
├── llm_client.py                 # OpenAI/Anthropic integration
├── blueprints/                   # Auto-generated tools
├── dashboard/                    # Visual monitoring
├── tests/                        # Comprehensive test suite
└── docs/                         # Full documentation

Implementation Plan

Phase 1: Branch & Replace

• Create branch: omen-intelligence-architecture
• Remove old implementation files
• Add all new architecture files
• Update README.md with new positioning
• Add ARCHITECTURE.md deep-dive

Phase 2: Testing & Validation

• Run test suite (all tests passing)
• Verify LLM integration (OpenAI + Anthropic)
• Test real-world TechCrunch monitoring workflow
• Launch dashboard and verify Blueprint Vault visualization

Phase 3: Documentation & PR

• Update all docs to reflect new architecture
• Create comprehensive PR description
• Add migration guide if needed
• Merge to main

Files to Add/Replace

Core Python Modules

• intelligentone_server.py
• simulation_lab.py
• blueprint_vault.py
• router_agent.py
• llm_client.py

Configuration

• requirements.txt
• .env.example
• .gitignore

Documentation

• README.md
• docs/ARCHITECTURE.md
• docs/GETTING_STARTED.md

Testing

• tests/test_intelligentone_server.py
• tests/test_simulation_lab.py
• tests/test_techcrunch_monitor.py

Dashboard

• dashboard/index.html
• dashboard/server.py

Success Criteria

• All tests passing
• LLM integration working (hypothesis generation + judgment)
• Dashboard displays Blueprint Vault in real-time
• TechCrunch monitoring test completes end-to-end
• Documentation is comprehensive and clear
• README positions intelligentOne as billion-dollar opportunity

Timeline

• Day 1: Branch creation, file replacement, initial commit
• Day 2: Testing, validation, bug fixes
• Day 3: PR creation, review, merge

Strategic Impact

This replacement positions intelligentOne as:

1. First-mover in self-evolving MCP agent platforms
2. Defensible through recursive improvement moat
3. Investor-ready with clear billion-dollar narrative

Questions?

Comment below or reach out to the team.

---

Labels: enhancement, breaking-change, architecture
Milestone: v2.0.0 - Omen Intelligence
Assignees: @groupthinking</issue_description>

Comments on the Issue (you are @copilot in this section)

• Fixes 🚀 Replace codebase with self-evolving Omen Intelligence architecture (MCP + LLM + Blueprint Vault + Dashboard) #2

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