genpark-agent-context-dynamic-compressor-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-agent-context-dynamic-compressor-skillcompress this long HTML output before adding it to context"
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-agent-context-dynamic-compressor-skill
🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation
📌 Overview & Capability
genpark-agent-context-dynamic-compressor-skill is a deterministic, high-performance, zero-dependency Python tool and native Model Context Protocol (MCP) server designed for autonomous AI agents, multi-agent frameworks (LangGraph, CrewAI, AutoGen, OpenAI Swarm), and developer environments (Cursor, Windsurf, Claude Desktop).
Executive Capability: Dynamic long-horizon context window compression engine pruning verbose tool outputs, HTML boilerplate & semantic redundancy by 40-70%.
⚡ Key Highlights
🐍 Zero External
pipDependencies: Implemented entirely with pure Python standard library for instant zero-overhead execution.🔌 Native Model Context Protocol (MCP): Plugs directly into any MCP-compliant client via JSON-RPC 2.0 stdio.
⚡ Sub-Millisecond Execution: Slashes token burn and latency by resolving routine agent tasks deterministically without frontier LLM round-trips.
🛡️ Production-Hardened: Comprehensive error handling, boundary validation, and telemetry.
Related MCP server: Context Engine
🏗️ Architecture
graph LR
Agent([🤖 Autonomous Agent / IDE]) -->|MCP Protocol / JSON-RPC| Server[⚡ genpark-agent-context-dynamic-compressor-skill Server]
Server --> Core[🧠 Deterministic Processing Core]
Core --> Out[📊 Actionable Result & Telemetry]
Out --> Agent🚀 Quickstart & Usage
1. Direct Python Client Execution
python example_usage.py2. Programmatic Integration
from client import AgentContextDynamicCompressor
client = AgentContextDynamicCompressor()
result = client.run_benchmark_compression()
print(result)🔌 Model Context Protocol (MCP) Setup
Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:
claude_desktop_config.json
{
"mcpServers": {
"genpark-agent-context-dynamic-compressor-skill": {
"command": "python",
"args": ["/path/to/genpark-agent-context-dynamic-compressor-skill/mcp_server.py"]
}
}
}Direct MCP Testing
python mcp_server.py --test📊 Technical Specifications
Parameter | Type | Required | Description |
|
| Yes | Primary context, code, schema, or content input |
|
| No | Execution flags, compression ratios, or risk bounds |
This server cannot be deployed
Maintenance
Related MCP Connectors
A paid remote MCP for OpenAI Codex context compressor, built to return verdicts, receipts, usage log
AI Reasoning Cache & Consensus Layer with 11 MCP tools via Streamable HTTP.
Deterministic AI agent microtools, no accounts/API keys. fetch_extract: 98% token cut. 38 tools.
- WauldoOAuthcom.wauldo
Stateless agentic tools over MCP: concept extraction, long-context, knowledge graph, planning.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceDeterministic context compression for MCP agents, reducing token usage via 11 tools for prompts, history, shell output, file deltas, and code navigation without ML or GPU.9MIT
- AlicenseNot gradedqualityCmaintenanceA task-aware context compression layer for Agent workflows, RAG pipelines, and AI Coding assistants, reducing noisy logs, retrieval chunks, and code context into high-signal LLM inputs via CLI, Python SDK, and MCP.352MIT
- FlicenseNot gradedqualityBmaintenanceEnables AI agents to compress retrieved web documents against a query, pruning irrelevant noise so only the most relevant context reaches the model. It runs as a zero-dependency Python MCP server that accepts a query payload and returns structured, scored output.7-
- FlicenseNot gradedqualityBmaintenanceEnables AI agents and developers to dynamically tokenize session context windows and compact dialogue history into structured outputs via MCP, CLI, or Python client. Runs on pure standard library Python with no external dependencies for deterministic, low-latency execution.8-