genpark-vector-clock-causal-ordering-skill
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
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-vector-clock-causal-ordering-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.gitRelated MCP Connectors
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