AEGIS Governance — MCP Server

MCP serverAI & models

Aegis adds governance to your AI: every action it wants to take is checked against six gates that return a PROCEED, PAUSE, or HALT decision. Each decision is logged in an audit trail where every entry is linked to the one before it, so the record of what your AI did cannot be quietly altered.

Available today. Use it from your connected AI after setup.

Add Aegis, then run your AI agent's actions through it so each one is checked at the gates and recorded in the audit trail.

Then ask your AI: use AEGIS Governance — MCP Server

What your AI can do with it

  • Check each action the AI wants to take against six governance gates
  • Get a clear PROCEED, PAUSE, or HALT decision for every action
  • Pause actions that should not move ahead right away
  • Halt actions that fail the checks
  • Log every decision to an audit trail where each entry is linked to the last
  • Review a complete record of what the AI tried to do and what was allowed

From the project's README

As published by undercurrentai/aegis-mcp in README.md.

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.

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.

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

ToolWhat it does
aegis_evaluate_proposalFull six-gate evaluation of a proposal; returns PROCEED/PAUSE/HALT/ESCALATE with per-gate scores and rationale
aegis_quick_risk_checkFast risk screen for a proposed change
aegis_check_thresholdsCurrent gate threshold configuration
aegis_get_scoring_guideDomain-specific guidance for deriving gate parameters (e.g. cicd)
aegis_record_proposalRecord a proposal for later verification
aegis_list_proposalsList recorded proposals
aegis_verify_proposalsVerify recorded proposals against outcomes
aegis_list_decisionsList past governance decisions
aegis_get_decisionFetch a specific decision with full audit detail
aegis_crypto_statusHash-chain audit integrity status

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


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Signals

Last commit
Jun 2026
Weekly downloads
167
Advanced
Delivery
aegis MCP server → your ahel gateway (mcp.ahel.ai) → every connected AI client.
Catalog kind
mcp-server
Gateway key
com-undercurrentholdings-aegis
Source
github.com/undercurrentai/aegis-mcp
Hosted endpoint
https://mcp.aegis.undercurrentholdings.com/mcp