genpark-agent-human-in-the-loop-approval-gate-skill
Integrates with CrewAI multi-agent frameworks to enforce risk tiers, reversible checkpoints, and token approval bounds.
Integrates with LangGraph multi-agent frameworks to enforce risk tiers, reversible checkpoints, and token approval bounds.
Integrates with OpenAI Swarm multi-agent frameworks to enforce risk tiers, reversible checkpoints, and token approval bounds.
Integrates with OpenAI Swarm multi-agent frameworks to enforce risk tiers, reversible checkpoints, and token approval bounds.
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-agent-human-in-the-loop-approval-gate-skill
🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation
📌 Overview & Capability
genpark-agent-human-in-the-loop-approval-gate-skill is a deterministic, high-performance, zero-dependency Python tool and native Model Context Protocol (MCP) server designed for autonomous AI agents, multi-agent frameworks (LangGraph, CrewAI, AutoGen, OpenAI Swarm), and developer environments (Cursor, Windsurf, Claude Desktop).
Executive Capability: Human-in-the-Loop policy gatekeeper enforcing risk tiers, reversible checkpoints & token approval bounds for autonomous agents.
⚡ Key Highlights
🐍 Zero External
pipDependencies: Implemented entirely with pure Python standard library for instant zero-overhead execution.🔌 Native Model Context Protocol (MCP): Plugs directly into any MCP-compliant client via JSON-RPC 2.0 stdio.
⚡ Sub-Millisecond Execution: Slashes token burn and latency by resolving routine agent tasks deterministically without frontier LLM round-trips.
🛡️ Production-Hardened: Comprehensive error handling, boundary validation, and telemetry.
Related MCP server: varco_mcp
🏗️ Architecture
graph LR
Agent([🤖 Autonomous Agent / IDE]) -->|MCP Protocol / JSON-RPC| Server[⚡ genpark-agent-human-in-the-loop-approval-gate-skill Server]
Server --> Core[🧠 Deterministic Processing Core]
Core --> Out[📊 Actionable Result & Telemetry]
Out --> Agent🚀 Quickstart & Usage
1. Direct Python Client Execution
python example_usage.py2. Programmatic Integration
from client import AgentHumanInTheLoopApprovalGate
client = AgentHumanInTheLoopApprovalGate()
result = client.run_benchmark_approval_gate()
print(result)🔌 Model Context Protocol (MCP) Setup
Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:
claude_desktop_config.json
{
"mcpServers": {
"genpark-agent-human-in-the-loop-approval-gate-skill": {
"command": "python",
"args": ["/path/to/genpark-agent-human-in-the-loop-approval-gate-skill/mcp_server.py"]
}
}
}Direct MCP Testing
python mcp_server.py --test📊 Technical Specifications
Parameter | Type | Required | Description |
|
| Yes | Primary context, code, schema, or content input |
|
| No | Execution flags, compression ratios, or risk bounds |
Related MCP Connectors
Pre-execution governance for AI agents. Deterministic PASS/FAIL/REVIEW verdicts, replayable proof.
Supervised API-write gateway for AI agents with policy, human approval and execution receipts.
Human-in-the-loop approval for agent actions, with verifiable action-bound receipts.
Zero-secret MCP gateway for AI agents: risk-scored, audited calls with human-in-the-loop approval.
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