genpark-agent-synthetic-data-differential-privacy-guard-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-synthetic-data-differential-privacy-guard-skill
⚡ Overview & Architectural Significance
genpark-agent-synthetic-data-differential-privacy-guard-skill provides zero-dependency, deterministic agentic execution safety, sandboxing, and financial circuit breakers engineered strictly using Python 3.9+ standard library.
🌟 Key Architectural Capabilities
Zero External Dependencies: Operates exclusively via pure Python (
math,re,collections,heapq,hashlib,json). Zero pip install overhead, zero C-extension compile errors.Enterprise Agent Safety Invariants: Implements formal defenses against destructive shell commands, prompt injections, runaway spend loops, differential privacy data leakage, and planning deadlocks.
Native Anthropic MCP Protocol: Compliant with standard JSON-RPC 2.0 stdio MCP specifications for Claude Desktop, Cursor, and Windsurf.
Related MCP server: genpark-agent-budget-token-spend-circuit-breaker-skill
🏗️ Architectural Safety State Machine
flowchart TD
UserPrompt["Incoming User Instruction / External Input"] --> InjectionGuard["Prompt Injection & Jailbreak Sentinel"]
InjectionGuard -->|Malicious Injection| BlockPrompt["Reject Prompt (400 Bad Request)"]
InjectionGuard -->|Safe Prompt| AgentPlanner["Autonomous Agent Planner / LLM Core"]
AgentPlanner --> CircuitBreaker["Token Spend & Cost Circuit Breaker"]
CircuitBreaker -->|Budget Exceeded| FreezeSpend["Freeze Execution & Alert Admin"]
CircuitBreaker -->|Within Budget| LoopDetector["Deadlock & Liveloss Loop Detector"]
LoopDetector -->|Infinite Loop Detected| BreakLoop["Inject Corrective Guidance & Reroute Plan"]
LoopDetector -->|Healthy Trajectory| CommandSandbox["Bash / Subprocess Sandbox Guard"]
CommandSandbox -->|Destructive / Traversal| BlockCmd["Block Execution (Security Violation)"]
CommandSandbox -->|Safe Command| ToolExec["Safe Tool Execution"]
ToolExec --> PrivacyGuard["Synthetic Data Differential Privacy Guard"]
PrivacyGuard --> SanitizedOutput["Sanitized Output & Verified Return"]🚀 Quickstart & Standalone Execution
Local Python Client Usage
from client import AgentSyntheticDataDifferentialPrivacyGuard
# Initialize engine
engine = AgentSyntheticDataDifferentialPrivacyGuard()
# Execute self-testing benchmark suite
result = engine.run_benchmark_differential_privacy()
print("Execution Result:", result)🔌 One-Click MCP Integration (Claude Desktop / Cursor)
Add to your claude_desktop_config.json or cursor.json:
{
"mcpServers": {
"genpark-agent-synthetic-data-differential-privacy-guard-skill": {
"command": "python",
"args": ["-u", "/path/to/genpark-agent-synthetic-data-differential-privacy-guard-skill/mcp_server.py"]
}
}
}📦 Smithery.ai & PyPI Deployment
This skill contains pre-configured smithery.yaml and pyproject.toml manifests. Install directly via pip:
pip install git+https://github.com/alphaparkinc/genpark-agent-synthetic-data-differential-privacy-guard-skill.gitRelated MCP Connectors
Deterministic runtime safety for AI agents: scan PII, gate tool actions, verify LLM output.
Autonomous agent guardrails, bash sandboxing, MCP rugpull diffing & prompt firewalls.
Real-time MCP & A2A prompt-injection, jailbreak, and tool-schema poisoning firewall for AI agents.
Responsible-AI guardrails for agents: scoring with policy, injection & PII detection, DPDP.
Related MCP Servers
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- AlicenseNot gradedqualityBmaintenanceEnables autonomous agents to enforce financial guardrails by tracking real-time token burn and cost velocity, automatically throttling or freezing execution when budgets are exceeded. It also blocks destructive shell commands, prompt injections, and runaway planning loops while sanitizing outputs before they are returned.7MIT
- AlicenseNot gradedqualityBmaintenanceEnables agents to apply differential privacy epsilon-noise generation and quasi-identifier redaction to tabular data releases, helping prevent re-identification during data sharing and synthetic data workflows.7MIT
- AlicenseNot gradedqualityBmaintenanceEnables detection of autonomous agent infinite tool loops, planning deadlocks, and liveloss cycles via trajectory state entropy analysis. Also provides safety guardrails for prompt injection, destructive commands, budget overruns, and data leakage.7MIT