genpark-agentic-negotiation-counter-offer-strategist-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-agentic-negotiation-counter-offer-strategist-skillthey offered $50k; counter with a 3-year deal capped at $40k"
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-agentic-negotiation-counter-offer-strategist-skill
🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation
🌟 Overview
genpark-agentic-negotiation-counter-offer-strategist-skill delivers robust, industrial-grade capabilities engineered for Autonomous Cognitive Agent Execution and Enterprise Self-Healing Operations. 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 Agentic Multi-Turn Negotiator & Counter-Offer Strategist (inspired by Consumer Commerce & Contract Negotiations). Calculates Zone of Possible Agreement (ZOPA), concession decay curves, reservation price thresholds, and structured bargaining counter-offers.
💡 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, Meta Muse, and Tencent WorkBuddy runtime frameworks.
Deterministic & Safe: Rigorous mathematical algorithms, cryptographic hashing, and automated self-healing rollback.
High Concurrency & Low Latency: In-memory state tracking, tree traversal, and optimized regex parsers.
Related MCP server: gametheory-mcp
🚀 Quickstart
1. Direct Python Usage
from client import AgenticNegotiationCounterOfferStrategist
client = AgenticNegotiationCounterOfferStrategist()
result = client.evaluate_and_counter_offer()
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-agentic-negotiation-counter-offer-strategist-skill": {
"command": "python",
"args": ["/absolute/path/to/genpark-agentic-negotiation-counter-offer-strategist-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
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Autonomous commerce for AI agents: discover, quote, order, pay, verify.
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