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Orchestrate MCP AI workflows with policy enforcement, auditing, and execution monitoring.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "MCPuppet" yet — see the docs or source repo.
Design a workflow orchestration plan for an AI application using multiple MCP servers. Requirements: restrict sensitive tools to post-approval use, log every tool call input and output, and trigger alerts automatically on failure.
A workflow plan including policy rules, audit flow, alerting mechanisms, and execution steps.
Analyze this set of MCP workflow tool-call logs, identify policy violations, failed nodes, and high-risk actions, then output a timeline-based audit summary with improvement suggestions.
A timeline-based audit report with abnormal calls, risk explanations, and optimization recommendations.
Create an operational monitoring plan for my MCP workflows that tracks task status, latency, retry counts, and failure rates, and define thresholds for triggering alerts.
An actionable list of monitoring metrics and alert rules for continuously tracking workflow health.
Orchestrate multiple MCP server tools for complex Python workflows with logic.
Control agent workflows with stateful primitives and persisted execution facts.
Build AI agent workflows and automate tasks using MCP-connected services.
Orchestrate AI agent workflows with dependencies, parallel execution, and failure policies.
Aggregate multiple MCP servers into a few fixed tools for AI apps.
Let AI see and control a computer for complex desktop automation.