Click 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-synthetic-data-differential-privacy-guard-skill anonymize customers.csv with epsilon 1.0 differential privacy and redact quasi-identifiers"
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-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.
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-jev-system1-subconscious-decision-skill
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"]from client import AgentSyntheticDataDifferentialPrivacyGuard
# Initialize engine
engine = AgentSyntheticDataDifferentialPrivacyGuard()
# Execute self-testing benchmark suite
result = engine.run_benchmark_differential_privacy()
print("Execution Result:", result)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"]
}
}
}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.gitThis server cannot be deployed
Deterministic runtime safety for AI agents: scan PII, gate tool actions, verify LLM output.
Responsible-AI guardrails for agents: scoring with policy, injection & PII detection, DPDP.
Security gateway for AI agents: policy, approval, and audited execution, no secrets shared.
The WAF for agents. Pattern-based + heuristic firewall scans prompts, RAG documents, tool argume...
