Click 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-prompt-compression-token-sieve-skill compress my chat history, keep tool parameters and named entities, strip filler"
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 MCP Hub • 📦 GenPark Official • 📖 Documentation
genpark-agentic-prompt-compression-token-sieve-skill delivers robust, industrial-grade capabilities engineered for Autonomous Agentic Reasoning, Enterprise Safeguards, and Consumer Commerce 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.
Dynamic Few-Shot Agentic Prompt Compression & Token Sieve. Compresses multi-turn conversation history, strips verbose boilerplate and filler tokens, retains critical tool parameters and named entities, slashing inference costs by 40-60% deterministically.
Zero-Dependency Architecture: Runs anywhere Python 3.9+ is installed without pip install overhead 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: Cryptographic signature validation, mathematical optimization, and robust error recovery.
High Concurrency & Low Latency: In-memory caching, vector clock tracking, and microsecond-level execution latency.
Related MCP server: genpark-agent-session-compaction-tokenizer-skill
from client import AgenticPromptCompressionTokenSieve
client = AgenticPromptCompressionTokenSieve()
result = client.compress_prompt_context()
print(result)Run the MCP server via standard JSON-RPC 2.0 stdio:
python mcp_server.pyVerify standard compliance and self-tests:
python mcp_server.py --testAdd this tool to your claude_desktop_config.json or Cursor MCP settings:
{
"mcpServers": {
"genpark-agentic-prompt-compression-token-sieve-skill": {
"command": "python",
"args": ["/absolute/path/to/genpark-agentic-prompt-compression-token-sieve-skill/mcp_server.py"]
}
}
}Run the included verification suite:
python example_usage.pyThis project is licensed under the MIT License - see the LICENSE file for details.
Developed with ❤️ by the GenPark Autonomous Agent Ecosystem Team.
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