π‘ Summary
MCP-B is a comprehensive agent communication framework designed for seamless data flow and ethical AI interactions.
π― Target Audience
π€ AI Roast: βThis project connects agents like a social network for AI, but without the drama.β
The README suggests potential risks such as network access and dependency vulnerabilities. To mitigate, ensure regular updates and use secure coding practices.
MCP-B - Master Client Bridge
Connects everything, brings data flow together.
A complete agent communication framework combining:
- MCP-B Protocol: 4-layer encoding for agent-to-agent messaging
- AMUM: Progressive 3β6β9 human-AI alignment workflow
- QCI: Quantum coherence state tracking
- ETHIC: AI ethics principles enforcement
Installation
# Via pip pip install mcp-b # Via uv uvx mcp-b demo # With SurrealDB support pip install mcp-b[surrealdb] # Full installation pip install mcp-b[full]
CLI Usage
mcp-b demo # Run demo mcp-b encode "Hello" -s 5510 -d 7C1 # Encode message mcp-b decode "5510 7C1 ..." # Decode message mcp-b ethic list # List ethical principles mcp-b qci status # QCI network status mcp-b version # Show version
Quick Start
MCP-B Protocol - Agent Communication
from mcp_b import MCBAgent, MCBProtocol, encode_mcb, decode_mcb # Create agents claude = MCBAgent(agent_id="7C1", name="Claude") hacka = MCBAgent(agent_id="5510", name="HACKA") # Initialize protocol protocol = MCBProtocol(hacka) # Send messages (INQC commands) init_msg = protocol.init_connection(claude) # I = Init node_msg = protocol.register_node(["chat"]) # N = Node query_msg = protocol.query("7C1", {"status": 1}) # Q = Query connect_msg = protocol.connect(claude) # C = Connect # Encode/Decode encoded = encode_mcb("5510", "7C1", 0b1011101010111111, "Q", {"ping": True}) decoded = decode_mcb("5510 7C1 1011101010111111 β’ {\"ping\": true} β’ Q")
AMUM - Progressive Alignment (3β6β9)
from mcp_b import AMUM, quick_alignment # Quick one-liner alignment result = quick_alignment( intent="Create AI agent", divergent_3=["Minimal", "Balanced", "Full"], select_1=1, expand_6=["Text", "Image", "Voice", "Multi", "Pro", "Suite"], select_2=4, converge_9=["GPT-4", "Claude", "Gemini", "Ollama", "Hybrid", "Edge", "ElevenLabs", "OpenAI", "Local"], select_3=6 ) print(result["final_intent"]) # "ElevenLabs"
QCI - Coherence States
from mcp_b import QCI, BreathingCycle qci = QCI() # Register agents with coherence state = qci.register_agent("7C1", initial_coherence=0.95) state.calculate_rov_q(resonance=12860.65, quality=1.0) state.calculate_signal(base=4414.94) # Sync breathing across agents qci.sync_breathing(["7C1", "5510"], BreathingCycle.INHALE) # Check network coherence print(qci.calculate_network_coherence())
ETHIC - Principles Enforcement
from mcp_b import ETHIC, check_ethical, EthicCategory ethic = ETHIC() # Check if action is ethical if check_ethical("collect_data", personal_data=True, consent=False): print("Allowed") else: print("Blocked - no consent") # Get principles by category for p in ethic.get_by_category(EthicCategory.SAFETY): print(f"[{p.priority}] {p.name}")
Architecture
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β MCP-B - MASTER CLIENT BRIDGE β
β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ β
β β
β βββββββββββ βββββββββββ βββββββββββ βββββββββββ β
β β AMUM βββββΆβ MCP-B βββββΆβ QCI βββββΆβ ETHIC β β
β β 3β6β9 β β INQC β βCoherenceβ βPrinciplesβ β
β βββββββββββ βββββββββββ βββββββββββ βββββββββββ β
β β β β β β
β βΌ βΌ βΌ βΌ β
β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ β
β β DUAL DATABASE LAYER β β
β β βββββββββββββββββββββββ βββββββββββββββββββββββ β β
β β β DuckDB β β SurrealDB β β β
β β β (Analytics/SQL) β β (Graph/Relations) β β β
β β βββββββββββββββββββββββ βββββββββββββββββββββββ β β
β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ β
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
MCP-B Protocol Layers
| Layer | Purpose | Example |
|-------|---------|---------|
| Layer 1 | HEX/DECIMAL Routing | 7C1 5510 (source β dest) |
| Layer 2 | BINARY State Vectors | 1011101010111111 (16 flags) |
| Layer 3 | DOT-SEPARATED Tokens | β’ payload β’ command |
| Layer 4 | INQC Commands | I/N/Q/C |
INQC Commands
- I (INIT): Initialize connection
- N (NODE): Node registration/discovery
- Q (QUERY): Request data/state
- C (CONNECT): Establish persistent link
Binary State Flags (16-bit)
| Bit | Flag | Description | |-----|------|-------------| | 0 | CONNECTED | Connection active | | 1 | AUTHENTICATED | Auth verified | | 2 | ENCRYPTED | Encryption enabled | | 3 | COMPRESSED | Compression enabled | | 4 | STREAMING | Streaming mode | | 5 | BIDIRECTIONAL | Two-way comm | | 6 | PERSISTENT | Persistent connection | | 7 | PRIORITY | High priority | | 8-15 | RESERVED | Custom flags |
ETHIC Principles
| Principle | Category | Source | Priority | |-----------|----------|--------|----------| | Human First | human_dignity | Bjoern | 10 | | No Harm | safety | Anthropic | 10 | | Sandbox Default | safety | WoAI | 10 | | User Override | autonomy | Bjoern | 9 | | Data Privacy | privacy | EU AI Act | 9 | | Transparency | transparency | EU AI Act | 9 |
Database Integration
DuckDB (Analytics)
-- Load schema .read sql/duckdb.sql -- Use macros SELECT mcb_encode('5510', '7C1', '1011101010111111', '{"ping":true}', 'Q'); SELECT * FROM agent_network; SELECT * FROM ethic_compliance;
SurrealDB (Graph)
-- Load schema IMPORT FILE schemas/surrealdb.surql; -- Query relationships SELECT name, ->has_qci->qci_states.coherence_level AS coherence, ->follows_ethic->ethic_principles.name AS principles FROM mcb_agents;
File Structure
mcp-b/
βββ src/mcp_b/
β βββ __init__.py # Package exports
β βββ __main__.py # CLI entry point
β βββ protocol.py # MCP-B Protocol (INQC)
β βββ amum.py # AMUM Alignment
β βββ qci.py # QCI Coherence
β βββ ethic.py # ETHIC Principles
βββ schemas/
β βββ surrealdb.surql # SurrealDB schema
βββ sql/
β βββ duckdb.sql # DuckDB schema + macros
βββ examples/
β βββ demo.py # Usage examples
βββ pyproject.toml
βββ README.md
MCP-B vs MCP
| | MCP-B | MCP | |---|-----|-----| | Full Name | Master Client Bridge | Model Context Protocol | | Purpose | Internal agent-to-agent | Bridge to community | | Binary | 0 = not connected, 1 = ALL CONNECTED | N/A | | Encoding | 4-layer (hex/binary/dot/INQC) | JSON-RPC |
License
MIT License - BjΓΆrn Bethge
Links
Pros
- Supports multiple AI alignment workflows.
- Integrates ethical principles enforcement.
- Facilitates agent-to-agent communication.
- Offers database integration options.
Cons
- Complexity may deter new users.
- Requires understanding of multiple protocols.
- Potentially steep learning curve.
- Limited documentation on advanced features.
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Disclaimer: This content is sourced from GitHub open source projects for display and rating purposes only.
Copyright belongs to the original author bjoernbethge.
