AEGIS Governance — MCP Server

Quantitative governance for AI agents and engineering decisions. AEGIS evaluates proposals through six quantitative gates — Risk, Profit, Novelty, Complexity, Quality, Utility — and returns a structured decision (PROCEED / PAUSE / HALT / ESCALATE) with confidence scores, rationale, and a hash-chained audit trail.

Give your agent a decision gate it can call before it acts — and an audit record compliance can actually read (NIST AI RMF, EU AI Act Annex IV).

• Works immediately, no signup: the local server runs in sandbox mode (10 evaluations/day).
• 6 local tools (evaluations, risk checks, health, decision history, usage) — 10 on the hosted server.
• Hosted server with hash-chained audit trails — free Community tier (100 evaluations/month, no credit card).
• Want to see it before connecting? Try the Advisor in your browser — no install, no signup.

Hosted server (streamable-http, full 10-tool surface)

Get a free API key at portal.undercurrentholdings.com (GitHub/Google sign-in, key provisioned automatically), then:

Claude Code

claude mcp add --transport streamable-http aegis https://mcp.aegis.undercurrentholdings.com/mcp \
  --header "Authorization: Bearer YOUR_API_KEY"

Cursor (.cursor/mcp.json) / Windsurf / any streamable-http MCP client:

{
  "mcpServers": {
    "aegis": {
      "type": "streamable-http",
      "url": "https://mcp.aegis.undercurrentholdings.com/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY"
      }
    }
  }
}

VS Code (.vscode/mcp.json):

{
  "servers": {
    "aegis": {
      "type": "http",
      "url": "https://mcp.aegis.undercurrentholdings.com/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY"
      }
    }
  }
}

Prefer a local SDK instead of MCP?

The Python SDK has a sandbox mode that works with no account at all (10 evaluations/day):

pip install aegis-governance

from aegis import Aegis

decision = Aegis().evaluate(
    proposal_summary="Add Redis caching layer to reduce API latency",
    risk_baseline=0.02, risk_proposed=0.05,
    novelty_score=0.75, complexity_score=0.8, quality_score=0.9,
)
print(decision.status)  # "proceed"

The local stdio MCP server above ships in aegis-governance >= 1.3.0 via the [mcp] extra.

Tools

Why a governance gate?

AI agents make thousands of decisions with no record of why. AEGIS gives every consequential action a quantitative evaluation and a tamper-evident audit entry — so "the agent decided to deploy" becomes a signed, replayable record with gate scores and rationale.

• Six gates: Risk, Profit, Novelty, Complexity, Quality, Utility — calibrated thresholds, KL-divergence drift detection
• Audit-ready: hash-chained decision log; NIST AI RMF and EU AI Act Annex IV artifact generation
• Five integration surfaces: MCP (this repo), Python SDK, REST API, CLI, GitHub Action

Links

• Docs: aegis.undercurrentholdings.com/docs · MCP tools reference
• Try it in the browser (no install): AEGIS Advisor
• Pricing: portal.undercurrentholdings.com/pricing — free Community tier; paid tiers for teams and regulated environments
• Source distribution: PyPI aegis-governance (BSL-1.1)

Built by Undercurrent — Agency over agents.

Quickstart (local, no account needed)

pip install "aegis-governance[mcp]"

Claude Code

claude mcp add aegis -- aegis-mcp-server

Cursor (.cursor/mcp.json) / Windsurf / any stdio MCP client:

{
  "mcpServers": {
    "aegis": { "command": "aegis-mcp-server" }
  }
}

VS Code (.vscode/mcp.json):

{
  "servers": {
    "aegis": { "type": "stdio", "command": "aegis-mcp-server" }
  }
}

Runs in sandbox mode out of the box. Set AEGIS_API_KEY in the server's environment (free key) to unlock decision history, usage reports, and risk checks. Requires Python >= 3.10.