genpark-proactive-intent-disambiguation-clarifier-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-proactive-intent-disambiguation-clarifier-skillI want to delete all inactive users before running the cleanup."
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-proactive-intent-disambiguation-clarifier-skill
🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation
🌟 Overview
genpark-proactive-intent-disambiguation-clarifier-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.
Proactive High-Stakes Intent Disambiguator & Option Clarifier. Detects under-specified user goals, assesses reversibility and risk, and constructs minimal multiple-choice clarifications before triggering autonomous execution.
💡 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: MCP Human Approval Gateway
🚀 Quickstart
1. Direct Python Usage
from client import ProactiveIntentDisambiguationClarifier
client = ProactiveIntentDisambiguationClarifier()
result = client.disambiguate_intent()
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-proactive-intent-disambiguation-clarifier-skill": {
"command": "python",
"args": ["/absolute/path/to/genpark-proactive-intent-disambiguation-clarifier-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
Decision-assurance for AI agents: an auditable action boundary + receipt before it acts.
Machine-verifiable authorization checks for autonomous AI agents.
Reversibility intelligence and durable exit evidence for AI agents before real-world commitments.
MERCATOR Verify: evidence-backed verification and decision support for autonomous agents.
Related MCP Servers
- AlicenseAqualityAmaintenanceRuntime budget authority for autonomous agents - a set of tools to check, reserve, spend, and release budget before and after every costly, risky operation. The agent asks "can I afford this?" before acting, and reports what it actually used afterward.961 npmApache 2.0
- AlicenseNot gradedqualityBmaintenanceA public-safe research prototype for controlling AI-agent tool actions with deterministic policy, risk-based human approval, time-bound authorization and a tamper-evident audit chain.1MIT
- AlicenseNot gradedqualityCmaintenanceEnables Alexa+ agents to maintain auditable operational continuity across shifts by turning speech into verifiable state, persisting unresolved work, refusing unverified actions, and requiring human approval before executing and confirming high-risk tasks.MIT
- FlicenseNot gradedqualityBmaintenanceEnables autonomous agents and multi-agent frameworks to route actions through deterministic human approval gates before execution. Enforces risk tiers, reversible checkpoints, and token approval bounds so that risky or costly operations can be reviewed, bounded, and rolled back without LLM round-trips.7-