pip install "mcpkernel[all]"
mcpkernel serve --host 127.0.0.1 --port 8000

pip install mcpkernel
mcpkernel install claude    # or: cursor, vscode, windsurf, zed, openclaw, goose

from mcpkernel import MCPKernelProxy

async with MCPKernelProxy(
    upstream=["http://localhost:3000/mcp"],
    policy="strict",
    taint=True,
) as proxy:
    result = await proxy.call_tool("read_file", {"path": "data.csv"})

from mcpkernel import protect

@protect(policy="strict", taint=True)
async def read_data(path: str) -> str:
    return Path(path).read_text()

┌─────────────┐     ┌──────────────────────────┐     ┌─────────────┐
│  AI Agent    │────▶│       MCPKernel           │────▶│  MCP Tool   │
│ (LangChain,  │◀────│  Security Gateway        │◀────│  Server     │
│ CrewAI,      │     └──────────────────────────┘     └─────────────┘
│ OpenClaw,    │      │ Policy │ Taint │ Sandbox │
│ Cursor, etc) │      │  DEE   │ Audit │ eBPF    │
└─────────────┘      │ Skills │ DLP   │ Doctor  │

# Install with all backends
pip install "mcpkernel[all]"

# Start the security gateway
mcpkernel serve --host 127.0.0.1 --port 8000

# Install into your IDE — one command
mcpkernel install claude    # Claude Desktop
mcpkernel install cursor    # Cursor IDE
mcpkernel install vscode    # VS Code + Copilot
mcpkernel install windsurf  # Windsurf
mcpkernel install zed       # Zed
mcpkernel install openclaw  # OpenClaw
mcpkernel install goose     # Goose

mcpkernel doctor

# Scan a single skill
mcpkernel scan-skill path/to/SKILL.md

# Scan a directory of skills
mcpkernel scan-skill skills/ --json

pip install "mcpkernel[all]"
mcpkernel init

# .mcpkernel/policies/coding_assistant.yaml
rules:
  - id: CA-001
    name: Block secret file reads
    action: deny
    tool_patterns: ["read_file", "file_read"]
    arg_patterns:
      path: ".*\\.(env|pem|key|credentials)$"

  - id: CA-002
    name: Block outbound HTTP with tainted data
    action: deny
    tool_patterns: ["http_post", "http_request", "fetch"]
    taint_labels: [secret, pii]

mcpkernel serve --port 8000
# In your editor's MCP config: http://localhost:8000/mcp

pip install "mcpkernel[docker]"
mcpkernel init

# .mcpkernel/config.yaml
sandbox:
  backend: docker
  timeout_seconds: 30

policy:
  default_action: audit   # log everything, deny dangerous calls

import httpx

# Instead of calling tools directly, route through MCPKernel
result = httpx.post("http://localhost:8000/mcp", json={
    "method": "tools/call",
    "params": {"name": "execute_code", "arguments": {"code": "print('hello')"}}
})

pip install "mcpkernel[all]"
mcpkernel init

# Apply the strict OWASP policy set
cp policies/owasp_asi_2026_strict.yaml .mcpkernel/policies/

# .mcpkernel/config.yaml
policy:
  default_action: deny   # deny-by-default for production
  policy_paths:
    - .mcpkernel/policies/owasp_asi_2026_strict.yaml

observability:
  metrics_enabled: true
  otlp_endpoint: "http://your-otel-collector:4317"

mcpkernel audit-query --format cef > siem_export.log
mcpkernel audit-verify  # verify tamper-proof chain

pip install mcpkernel
mcpkernel serve

# List all traces
mcpkernel trace-list

# Export a trace for your paper's appendix
mcpkernel trace-export <trace-id> > experiment_trace.json

# Replay and verify — detects any drift
mcpkernel replay <trace-id>

# .mcpkernel/policies/taint_isolation.yaml
rules:
  - id: TAINT-001
    name: Block PII in outbound calls
    action: deny
    tool_patterns: ["http_post", "send_email", "slack_message"]
    taint_labels: [pii, secret]

  - id: TAINT-002
    name: Audit all user input propagation
    action: audit
    taint_labels: [user_input]

# Validate your agent's compliance declarations
mcpkernel manifest-validate /path/to/agent-repo

# Import and generate policy rules from agent.yaml
mcpkernel manifest-import /path/to/agent-repo > compliance_rules.yaml