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genpark-paged-attention-kv-cache-budget-calculator-skill

by alphaparkinc
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

genpark-paged-attention-kv-cache-budget-calculator-skill

Python 3.9+ License MIT MCP Compatible GenPark AI Zero Dependencies

🌐 GenPark MCP Hub Showcase • 📦 GenPark Official Website • 📖 Documentation

📌 Overview & Capability

genpark-paged-attention-kv-cache-budget-calculator-skill is a deterministic, zero-dependency Python skill engineered for autonomous AI agents, multi-agent frameworks (Claude Desktop, Cursor, AutoGPT, CrewAI), and enterprise pipelines.

Executive Capability: PagedAttention chunked prefill KV cache & GPU memory allocator (vLLM)

⚡ Key Highlights & Value

  • 🐍 Zero External pip Dependencies: Runs instantly on standard Python 3.9+ with zero environment bloat.

  • 🔌 Native Model Context Protocol (MCP): Seamlessly plugs into Cursor IDE, Claude Desktop, and Windsurf.

  • 🎯 Deterministic & Reliable: 100% predictable input/output contracts with full JSON Schema validation.

  • 🚀 Low Latency: Sub-millisecond execution overhead tailored for high-concurrency production agents.

Related MCP server: genpark-agent-speculative-decoding-orchestrator-skill

🏗️ Architecture & Workflow

graph LR
    User([🌐 User / AI Agent]) -->|JSON-RPC Request| MCP[⚡ MCP Server / CLI]
    MCP --> Client[🛠️ Skill Client Core Engine]
    Client --> Engine[🧠 Algorithmic Execution Kernel]
    Engine --> Output[📊 Structured Output Dossier & Telemetry]
    Output --> User

🚀 Quickstart & Usage

1. Direct Python Client Execution

python example_usage.py

2. Programmatic Integration

from client import PagedAttentionKvCacheBudgetCalculatorClient

client = PagedAttentionKvCacheBudgetCalculatorClient()
result = client.calculate_kv_cache_allocation()
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-paged-attention-kv-cache-budget-calculator-skill": {
      "command": "python",
      "args": ["/path/to/genpark-paged-attention-kv-cache-budget-calculator-skill/mcp_server.py"]
    }
  }
}

📊 Technical Specifications

Parameter

Type

Required

Description

query_payload

string / dict

Yes

Primary input parameter parsed and executed deterministically

output_format

json / dict

Yes

Standardized response schema containing execution telemetry

❓ 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 1,160+ open-source, production-ready AI Agent skills at the GenPark AI MCP Hub and learn more about agentic shopping and commerce at GenPark AI.

Q3: How do I test this MCP server locally?

Run python mcp_server.py --test to verify MCP protocol discovery and tool schema negotiation.

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Maintenance

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Resources

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  • genpark-kv-cache-paged-attention-allocator-skill by alphaparkinc

    genpark-kv-cache-pagedofficial

    alphaparkinc
    A
    license
    Not graded
    quality
    B
    maintenance
    Enables MCP clients to manage LLM inference memory through virtual paged-attention KV-cache block mapping, non-contiguous physical page allocation, zero-copy fragmentation tracking, radix-trie prefix caching, INT8 quantized compute, and speculative decoding verification. It also exposes prefill and decode latency telemetry so edge deployments can be benchmarked and tuned without external dependencies.
    7
    MIT