This gives you IAM-style policy enforcement between agent pairs in multi-agent systems. You define rules like "orchestrator may only call billing agent when amount is under 1000" and gate every agent-to-agent call through an evaluate_call check. It logs decisions with signed attestations, which is useful if you're tracking EU AI Act Article 14 audit trails or ISO 42001 compliance. The core is really just policy definition, permission inheritance, and runtime evaluation. Reach for this when you're building agent orchestration and need to restrict which agents can invoke which other agents under what conditions, rather than letting everything talk to everything.
Per-agent-pair IAM for A2A
Per-agent-pair IAM for A2A. Define policies ('orchestrator may call billing only when amount<1000'), gate every A2A call via evaluate_call. EU AI Act Art 14 + ISO 42001 Annex A.7 evidence with signed policy-decision attestations.
# Install via pip
pip install agent_policy_enforcement_mcp
# Or install via Smithery
npx -y @smithery/cli@latest install agent-policy-enforcement-mcp --client claude
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|---|---|
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Add to your claude_desktop_config.json (Claude Desktop) or your MCP client config:
{
"mcpServers": {
"agent-policy-enforcement-mcp": {
"command": "uvx",
"args": ["agent-policy-enforcement-mcp"]
}
}
}
Or: pip install agent-policy-enforcement-mcp then run the agent-policy-enforcement-mcp command (stdio transport).
Once configured, ask your assistant, for example:
define_policy to …"evaluate_call to …"list_policies to …"io.github.ericm1018/skillfm-llm-cost-optimizer-openai-anthropic-usage
io.github.mikerawsonnz/llm-orchestration-agent
io.github.mikerawsonnz/authenticated-llm-agent
labforgedev/copilot-memory-mcp
csoai-org/agent-prompt-injection-firewall-mcp
io.github.mikerawsonnz/authenticated-multi-llm-agent