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-skillBuyer offers $12k for my car listed at $15k; what's a smart counter-offer?"
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: Taasky MCP server
🚀 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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- AlicenseNot gradedqualityBmaintenanceEnables autonomous multi-turn negotiation and counter-offer strategy by calculating ZOPA, concession decay curves, reservation price thresholds, and structured bargaining offers.7MIT

Taasky MCP serverofficial
AlicenseNot gradedqualityDmaintenanceEnables an agent to open and manage small negotiations with another party — their agent or a person in a plain browser — proposing, countering, and settling terms like a meeting time, a shift swap, or a price. Accepting, turning down, and withdrawing always wait for the user's confirmation, and the user's private limits are never exposed.Apache 2.0- AlicenseNot gradedqualityBmaintenanceEnables AI agents to resolve multi-merchant Universal Commerce Protocol catalogs with Pareto-optimal pricing and SLA routing, while supporting tokenized payment delegation and automated bargaining through MCP tools.7MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to run automated Rubinstein alternating-offers bargaining and multi-item bundling negotiations with merchant gateways over MCP STDIO JSON-RPC. Supports game-theoretic surplus splitting and agentic commerce negotiation workflows.7MIT