AEGIS Governance — MCP Server
MCP serverAI & modelsAegis 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.
Needs your own account with this service. Credentials stay encrypted.
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
| Tool | What it does |
|---|---|
aegis_evaluate_proposal | Full six-gate evaluation of a proposal; returns PROCEED/PAUSE/HALT/ESCALATE with per-gate scores and rationale |
aegis_quick_risk_check | Fast risk screen for a proposed change |
aegis_check_thresholds | Current gate threshold configuration |
aegis_get_scoring_guide | Domain-specific guidance for deriving gate parameters (e.g. cicd) |
aegis_record_proposal | Record a proposal for later verification |
aegis_list_proposals | List recorded proposals |
aegis_verify_proposals | Verify recorded proposals against outcomes |
aegis_list_decisions | List past governance decisions |
aegis_get_decision | Fetch a specific decision with full audit detail |
aegis_crypto_status | Hash-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
- 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.
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