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openclaw-genpark-latent-action-engine

MCP

Enables MCP clients to perform speculative tree-search action prediction and prefetching using Laplace-smoothed Markov transitions, returning deterministic JSON tool results.

@alphaparkinc · MIT License · updated today

SECURITY

B

SCORE

60

STARS

▲ 7

PLUG IN

git clone https://github.com/alphaparkinc/openclaw-genpark-latent-action-engine.git

See the README to configure this MCP server

README

openclaw-genpark-latent-action-engine

Organization Model Context Protocol Zero-Dependency Python Version Security Rating License

Production-hardened OpenClaw GenPark Agent skill engineered strictly with Python standard library. Provides high-throughput deterministic execution, cryptographic security, and game-theoretic optimization via the Model Context Protocol (MCP).


Architecture & Data Flow

flowchart TD
    Input[Incoming Agent Telemetry / Task Input] --> Core["OpenClawGenParkLatentActionEngine"]
    Core --> Model[Mathematical Logic Engine]
    Model --> MCPResponse[Deterministic JSON Output / MCP Tool Result]

Quickstart & Usage

1. Direct Python Client Execution

python example_usage.py

2. Standalone MCP Server Launch

Run the native STDIO MCP server:

python mcp_server.py

License

This project is licensed under the MIT License - see the LICENSE file for details.

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