genpark-agent-tool-dag-speculative-prefetcher-skill
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
genpark-agent-tool-dag-speculative-prefetcher-skill
π GenPark MCP Hub β’ π¦ GenPark Official β’ π Documentation
π Overview & Capability
genpark-agent-tool-dag-speculative-prefetcher-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: Topological tool dependency DAG scheduler and speculative prefetcher resolving agent tool execution graphs with parallel batching and speculative argument evaluation.
β‘ Key Highlights
π Zero External
pipDependencies: Implemented entirely with pure Python standard library for instant zero-overhead execution.π Native Model Context Protocol (MCP): Plugs directly into any MCP-compliant client via JSON-RPC 2.0 stdio.
β‘ Sub-Millisecond Execution: Slashes token burn and latency by resolving routine agent tasks deterministically without frontier LLM round-trips.
π‘οΈ Production-Hardened: Comprehensive error handling, boundary validation, and telemetry.
Related MCP server: AgentTasker MCP Server
ποΈ Architecture
graph LR
Agent([π€ Autonomous Agent / IDE]) -->|MCP Protocol / JSON-RPC| Server[β‘ genpark-agent-tool-dag-speculative-prefetcher-skill Server]
Server --> Core[π§ Deterministic Processing Core]
Core --> Out[π Actionable Result & Telemetry]
Out --> Agentπ Quickstart & Usage
1. Direct Python Client Execution
python example_usage.py2. Programmatic Integration
from client import AgentToolDAGPrefetcher
client = AgentToolDAGPrefetcher()
result = client.run_dag_benchmark()
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-agent-tool-dag-speculative-prefetcher-skill": {
"command": "python",
"args": ["/path/to/genpark-agent-tool-dag-speculative-prefetcher-skill/mcp_server.py"]
}
}
}Direct MCP Testing
python mcp_server.py --testπ Technical Specifications
Parameter | Type | Required | Description |
|
| Yes | Primary context, code, schema, or content input |
|
| No | Execution flags, compression ratios, or risk bounds |
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelogβ¦
Related MCP Connectors
MCP server for your apps' tools and custom tools, plus hosted AI agents and approval-gated workflows
The OpenRouter for tools. One MCP connection gives any AI agent 254 hosted tools, pay per call.
AI Reasoning Cache & Consensus Layer with 11 MCP tools via Streamable HTTP.
MCP server for progressive tool usage at any scale (see https://klavis.ai)
Related MCP Servers
- AlicenseBqualityBmaintenanceMCP server that lets AI models invoke CLI agents (Gemini, Codex, Claude, OpenCode) as tools β with parallel execution, retries, and structured output parsing.11,276 PyPI3MIT
- AlicenseBqualityCmaintenanceA lightweight stdio-only MCP server that allows AI agents to run multiple tasks (e.g., Python code, HTTP requests, shell commands) in parallel or with dependencies, returning structured results in a single call.247 PyPIMIT
- AlicenseAqualityBmaintenanceEnables building and running zero-dependency MCP servers with automatic JSON Schema generation, exposing Python tools to Claude Desktop, Cursor, and autonomous agent fleets.2Apache 2.0
- FlicenseNot gradedqualityBmaintenanceEnables agents and MCP-compliant clients to resolve tool execution graphs by scheduling calls as a topological dependency DAG, running independent tools in parallel batches while speculatively prefetching and evaluating arguments. This cuts latency and token burn by handling routine tool orchestration deterministically without extra LLM round-trips.7-