genpark-agent-execution-tree-rollback-checkpointer-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-agent-execution-tree-rollback-checkpointer-skillcheckpoint the current execution tree, then roll back if this path fails"
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-agent-execution-tree-rollback-checkpointer-skill
🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation
🌟 Overview
genpark-agent-execution-tree-rollback-checkpointer-skill delivers robust, industrial-grade capabilities for autonomous agents, copilot frameworks, and personal assistant architectures. Built exclusively on the Python standard library with zero external runtime dependencies, it integrates seamlessly as a native Model Context Protocol (MCP) server or an importable Python module.
Autonomous Agent Tree-of-Thoughts (ToT) State Rollback Checkpointer. Allows long-running personal agents (Manus, Devin) to snapshot environment/context states, evaluate branch viability, and backtrack when an exploration path fails without restarting from scratch.
💡 Key Capabilities
Zero-Dependency Architecture: Runs anywhere Python 3.9+ is installed without
pip installoverhead or supply-chain vulnerabilities.Model Context Protocol (MCP) First: Compatible with Claude Desktop, Cursor, GenPark Engine, and custom agentic frameworks.
Deterministic & Safe: Designed with strict validation, graceful error handling, and structured telemetry.
High Concurrency & Low Latency: In-memory caching and optimized data structures for real-time agent execution loops.
Related MCP server: undo
🚀 Quickstart
1. Direct Python Usage
from client import ExecutionTreeRollbackCheckpointer
client = ExecutionTreeRollbackCheckpointer()
result = client.snapshot_checkpoint()
print(result)2. Standalone MCP Server Execution
Run the MCP server via standard JSON-RPC 2.0 stdio:
python mcp_server.pyVerify standard compliance and self-tests:
python mcp_server.py --test3. Claude Desktop / Cursor MCP Configuration
Add this tool to your claude_desktop_config.json or Cursor MCP settings:
{
"mcpServers": {
"genpark-agent-execution-tree-rollback-checkpointer-skill": {
"command": "python",
"args": ["/absolute/path/to/genpark-agent-execution-tree-rollback-checkpointer-skill/mcp_server.py"]
}
}
}🛠️ Verification & Testing
Run the included verification suite:
python example_usage.py📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
Developed with ❤️ by the GenPark Autonomous Agent Ecosystem Team.
This server cannot be deployed
Maintenance
Related MCP Connectors
Agent checkpoints. Resume after context resets and handoffs with retry-safe, versioned saves.
Persistent memory and knowledge graphs for AI agents. Hybrid search, context checkpoints, and more.
Decision memory for AI agents: record, revisit, and resolve consequential choices.
Bounded agent exploration with persistent progress, deterministic receipts, and return contracts.
Related MCP Servers
- FlicenseAqualityDmaintenanceEnables Claude agents to checkpoint their context state and reset back to saved points with handoff messages, maintaining clean context windows during complex tasks by avoiding fragmented summaries from compactions.52-
- AlicenseAqualityCmaintenanceProvides checkpoint and rollback capabilities for AI agents, reversing file system changes and recording network mutations.164 npm1MIT
- AlicenseNot gradedqualityAmaintenanceEnables AI coding agents to checkpoint workspace state, branch parallel attempts, inspect diffs, and roll back to known-good states mid-task through MCP tools, with automatic safety checkpoints to prevent data loss.4 npmMIT
- FlicenseNot gradedqualityBmaintenanceEnables AI agents to persist and restore checkpoint state, supporting crash recovery and replay for LangGraph-based workflows.8-