genpark-vector-clock-causal-ordering-skill
OfficialClick on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@genpark-vector-clock-causal-ordering-skillcompare these vector clocks and tell me which updates are causally ordered"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
genpark-vector-clock-causal-ordering-skill
⚡ Overview & Architectural Significance
genpark-vector-clock-causal-ordering-skill provides mathematically proven distributed systems, consensus coordination, and conflict-free replication primitives engineered strictly using Python 3.9+ standard library.
🌟 Key Architectural Capabilities
Zero External Dependencies: Operates exclusively via pure Python (
time,math,json,uuid). Zero socket/grpc compilation overhead, zero external dependencies.Enterprise Distributed Invariants: Implements formal LWW CRDT state reconciliation, Raft leader election terms & quorum validation, Vector Clock happens-before causal graphs, token-bucket gossip dissemination, and ACID 2-phase commit atomic coordination.
Native Anthropic MCP Protocol: Compliant with standard JSON-RPC 2.0 stdio MCP specifications for Claude Desktop, Cursor, and Windsurf.
Related MCP server: genpark-crdt-lww-element-set-sync-skill
🏗️ Architectural Topology & State Machine
flowchart TD
ClientAgent["Client Agent Swarm Node"] --> VectorClock["Vector Clock Causal Stamping"]
VectorClock --> GossipNode["Token-Bucket Epidemic Gossip"]
GossipNode --> CRDTMerge["LWW CRDT Element State Reconciliation"]
CRDTMerge --> ConsensusCoordinator["Raft Leader / 2PC Transaction Coordinator"]
ConsensusCoordinator --> QuorumValidation["Quorum Voting & Commit Log Commit"]
QuorumValidation --> SwarmConvergence["Deterministic Swarm Convergence"]🚀 Quickstart & Standalone Execution
Local Python Client Usage
from client import VectorClockCausalTracker
# Initialize engine
engine = VectorClockCausalTracker()
# Execute self-testing benchmark suite
result = engine.benchmark_causal_tracking()
print("Execution Result:", result)🔌 One-Click MCP Integration (Claude Desktop / Cursor)
Add to your claude_desktop_config.json or cursor.json:
{
"mcpServers": {
"genpark-vector-clock-causal-ordering-skill": {
"command": "python",
"args": ["-u", "/path/to/genpark-vector-clock-causal-ordering-skill/mcp_server.py"]
}
}
}📦 Smithery.ai & PyPI Deployment
This skill contains pre-configured smithery.yaml and pyproject.toml manifests. Install directly via pip:
pip install git+https://github.com/alphaparkinc/genpark-vector-clock-causal-ordering-skill.gitThis server cannot be deployed
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