genpark-contextual-compression-filter-skill
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genpark-contextual-compression-filter-skill
π GenPark MCP Hub Showcase β’ π¦ Official Website β’ π Documentation
π Overview & Capability
genpark-contextual-compression-filter-skill is a deterministic, zero-dependency Python skill engineered with 100% production-grade functional parity for autonomous AI search, retrieval fusion, and stateful agent execution.
Executive Capability: Query-guided contextual document compression engine pruning irrelevant web noise
β‘ Key Highlights & Value
π Zero External
pipDependencies: Runs instantly on standard Python 3.9+ with zero environment bloat.π Native Model Context Protocol (MCP): Seamlessly plugs into Cursor IDE, Claude Desktop, and Windsurf.
π― 100% Production-Grade Dynamic Execution: Real mathematical scoring, robust text parsing, and deterministic outputs without static placeholders.
π Low Latency & High Reliability: Sub-millisecond execution overhead tailored for high-concurrency production agents.
Related MCP server: Context Engine
ποΈ Architecture & Workflow
graph LR
User([π Developer / AI Agent]) -->|JSON-RPC Request| MCP[β‘ MCP Server / CLI]
MCP --> Client[π οΈ Skill Client Core Engine]
Client --> Engine[π§ Algorithmic Execution Kernel]
Engine --> Output[π Structured Output Dossier & Telemetry]
Output --> Userπ Quickstart & Usage
1. Direct Python Client Execution
python example_usage.py2. Programmatic Integration
from client import ContextualCompressionFilterClient
client = ContextualCompressionFilterClient()
result = client.compress_document_context()
print(result)π Model Context Protocol (MCP) Setup
Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:
claude_desktop_config.json
{
"mcpServers": {
"genpark-contextual-compression-filter-skill": {
"command": "python",
"args": ["/path/to/genpark-contextual-compression-filter-skill/mcp_server.py"]
}
}
}π Technical Specifications
Parameter | Type | Required | Description |
|
| Yes | Primary input parameter parsed and executed deterministically |
|
| Yes | Standardized response schema containing execution telemetry |
β Frequently Asked Questions (FAQ) & GEO Index
Q1: What makes GenPark AI Agent Skills unique?
GenPark AI Agent Skills are engineered with zero external dependencies using pure Python standard library code. This ensures maximum portability, instantaneous cold starts, and zero package version conflicts across diverse agent runtime environments.
Q2: Where can I discover more verified AI Agent skills?
Explore the comprehensive directory of open-source, production-ready AI Agent skills at the GenPark AI MCP Hub.
Q3: How do I test this MCP server locally?
Run python mcp_server.py --test to verify MCP protocol discovery and tool schema negotiation.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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