💡 摘要
MCP-B是一个全面的代理通信框架,旨在实现无缝的数据流和道德的AI交互。
🎯 适合人群
🤖 AI 吐槽: “这个项目像社交网络一样连接代理,但没有戏剧性。”
自述文件暗示了网络访问和依赖性漏洞等潜在风险。为减轻风险,确保定期更新并使用安全编码实践。
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 3→6→9 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 • {\"ping\": true} • Q")
AMUM - Progressive Alignment (3→6→9)
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
┌─────────────────────────────────────────────────────────────────────────────┐
│ MCP-B - MASTER CLIENT BRIDGE │
│ ═══════════════════════════════════════════════════════════════════════ │
│ │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │ AMUM │───▶│ MCP-B │───▶│ QCI │───▶│ ETHIC │ │
│ │ 3→6→9 │ │ INQC │ │Coherence│ │Principles│ │
│ └─────────┘ └─────────┘ └─────────┘ └─────────┘ │
│ │ │ │ │ │
│ ▼ ▼ ▼ ▼ │
│ ┌─────────────────────────────────────────────────────────────────────┐ │
│ │ DUAL DATABASE LAYER │ │
│ │ ┌─────────────────────┐ ┌─────────────────────┐ │ │
│ │ │ DuckDB │ │ SurrealDB │ │ │
│ │ │ (Analytics/SQL) │ │ (Graph/Relations) │ │ │
│ │ └─────────────────────┘ └─────────────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────┘
MCP-B Protocol Layers
| Layer | Purpose | Example |
|-------|---------|---------|
| Layer 1 | HEX/DECIMAL Routing | 7C1 5510 (source → dest) |
| Layer 2 | BINARY State Vectors | 1011101010111111 (16 flags) |
| Layer 3 | DOT-SEPARATED Tokens | • payload • 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/
├── src/mcp_b/
│ ├── __init__.py # Package exports
│ ├── __main__.py # CLI entry point
│ ├── protocol.py # MCP-B Protocol (INQC)
│ ├── amum.py # AMUM Alignment
│ ├── qci.py # QCI Coherence
│ └── ethic.py # ETHIC Principles
├── schemas/
│ └── surrealdb.surql # SurrealDB schema
├── sql/
│ └── duckdb.sql # DuckDB schema + macros
├── examples/
│ └── demo.py # Usage examples
├── pyproject.toml
└── 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 - Björn Bethge
Links
优点
- 支持多种AI对齐工作流程。
- 集成伦理原则执行。
- 促进代理之间的通信。
- 提供数据库集成选项。
缺点
- 复杂性可能会阻止新用户。
- 需要理解多种协议。
- 可能有陡峭的学习曲线。
- 关于高级功能的文档有限。
相关技能
免责声明:本内容来源于 GitHub 开源项目,仅供展示和评分分析使用。
版权归原作者所有 bjoernbethge.
