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kakunin

kakunin

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github.com/nqzaiSecurity1Updated 7w ago
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About kakunin

kakunin is an MCP server for AI agents that checks X.509 identity, authorization scope, and risk through the Kakunin compliance API. It also records behavioral events in an immutable audit log.

Key features

  1. 01Verify certificate scope and action limits
  2. 02Check rolling risk scores and trends
  3. 03Append events to an immutable audit log
  4. 04Trigger revocation checks for high-scoring events

Use cases

  1. 01Check authorization before an agent performs an action
  2. 02Review an agent’s current risk score
  3. 03Record agent behavior for audit

MCP support

Exposes tools for scope verification, risk-score lookup, and audit-log appending to MCP clients. It acts on behalf of an agent using Kakunin API credentials and an Agent ID.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "nqzai-kakunin": {
      "command": "npx",
      "args": [
        "-y",
        "@kakunin/mcp"
      ],
      "env": {
        "KAKUNIN_API_KEY": "<KAKUNIN_API_KEY>",
        "KAKUNIN_AGENT_ID": "<KAKUNIN_AGENT_ID>"
      }
    }
  }
}
Run commandnpx -y @kakunin/mcp
Environment variables to set
KAKUNIN_API_KEYSecretKakunin API key (kak_live_... or kak_test_...). Obtain at https://kakunin.ai/dashboard.
KAKUNIN_AGENT_IDID of the agent this MCP server acts on behalf of (agt_...).

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npm@kakunin/mcp0.2.3

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

@kakunin/mcp — MCP server for AI agent identity & compliance

OpenSSF Scorecard kakunin-mcp MCP server

kakunin-mcp MCP server

Model Context Protocol server for the Kakunin AI agent compliance API. Lets AI agents self-verify scope, check their own risk score, and log behavioral events — all from within Claude, Cursor, or any MCP-compatible runtime.

npx @kakunin/mcp

Tools

verify_agent_scope

Check whether this agent is authorised to perform an action before executing it. Verifies the active X.509 certificate, permitted_actions scope, financial limits, and revocation status.

{
  "action": "initiate EUR/USD trade on euronext for 50000 USD",
  "venue": "euronext",
  "amount_usd": 50000
}

Returns { allowed: true|false, reason, certificate_status, permitted_actions }.

check_risk_score

Retrieve the agent's rolling 30-day risk score, band (low/medium/high), drift trend, and actionable guidance. No input required.

audit_log_append

Append a behavioral event to the agent's immutable audit log. Returns risk score + transaction ID. Events scoring ≥ 0.85 auto-trigger a certificate revocation check.

{
  "action_type": "transaction_initiated",
  "details": { "amount_usd": 50000, "venue": "NYSE" }
}

Setup

Prerequisites: Node ≥ 18, Kakunin API key + Agent ID from kakunin.ai/dashboard.

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "kakunin": {
      "command": "npx",
      "args": ["-y", "@kakunin/mcp"],
      "env": {
        "KAKUNIN_API_KEY": "kak_live_...",
        "KAKUNIN_AGENT_ID": "agt_..."
      }
    }
  }
}
Environment variables
VariableRequiredDescription
KAKUNIN_API_KEY✅API key (kak_live_... or kak_test_...)
KAKUNIN_AGENT_ID✅Agent ID the server acts on behalf of
KAKUNIN_BASE_URLoptionalOverride API base (default: https://kakunin.ai)

Sandbox mode

Use a kak_test_... key for development — hits the sandbox CA, no cost, 100 free certs/day.

Full docs at docs.kakunin.ai.

Contributors

Thanks to everyone who contributes (emoji key) — code and non-code alike:

Contributions of any kind are welcome — this project follows the all-contributors spec.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.