# Aegis Mcp

> Six-gate governance for AI agents: PROCEED/PAUSE/HALT decisions with hash-chained audit trails.

- **Type:** MCP server
- **Install:** `agentstack add mcp-undercurrentai-aegis-mcp`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [undercurrentai](https://agentstack.voostack.com/s/undercurrentai)
- **Installs:** 0
- **Category:** [Integrations](https://agentstack.voostack.com/c/integrations)
- **Latest version:** 1.3.0
- **License:** MIT
- **Upstream author:** [undercurrentai](https://github.com/undercurrentai)
- **Source:** https://github.com/undercurrentai/aegis-mcp
- **Website:** https://aegis.undercurrentholdings.com

## Install

```sh
agentstack add mcp-undercurrentai-aegis-mcp
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# AEGIS Governance — MCP Server

[](https://pypi.org/project/aegis-governance/)
[](https://aegis.undercurrentholdings.com/docs)

**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](https://portal.undercurrentholdings.com/signup?product=aegis&utm_source=github&utm_medium=aegis-mcp) (100 evaluations/month, no credit card).
- Want to see it before connecting? [Try the Advisor in your browser](https://aegis.undercurrentholdings.com/advisor) — no install, no signup.

## Quickstart (local, no account needed)

```bash
pip install "aegis-governance[mcp]"
```

**Claude Code**

```bash
claude mcp add aegis -- aegis-mcp-server
```

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

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

**VS Code** (`.vscode/mcp.json`):

```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](https://portal.undercurrentholdings.com/signup?product=aegis&utm_source=github&utm_medium=aegis-mcp))
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](https://portal.undercurrentholdings.com/signup?product=aegis&utm_source=github&utm_medium=aegis-mcp) (GitHub/Google sign-in, key provisioned automatically), then:

**Claude Code**

```bash
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:

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

**VS Code** (`.vscode/mcp.json`):

```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):

```bash
pip install aegis-governance
```

```python
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](https://aegis.undercurrentholdings.com/docs) · [MCP tools reference](https://aegis.undercurrentholdings.com/docs/api/mcp-tools)
- **Try it in the browser** (no install): [AEGIS Advisor](https://aegis.undercurrentholdings.com/advisor)
- **Pricing**: [portal.undercurrentholdings.com/pricing](https://portal.undercurrentholdings.com/pricing) — free Community tier; paid tiers for teams and regulated environments
- **Source distribution**: [PyPI `aegis-governance`](https://pypi.org/project/aegis-governance/) (BSL-1.1)

---

Built by [Undercurrent](https://undercurrentholdings.com) — *Agency over agents.*

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [undercurrentai](https://github.com/undercurrentai)
- **Source:** [undercurrentai/aegis-mcp](https://github.com/undercurrentai/aegis-mcp)
- **License:** MIT
- **Homepage:** https://aegis.undercurrentholdings.com

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v1.3.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **1.3.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/mcp-undercurrentai-aegis-mcp
- Seller: https://agentstack.voostack.com/s/undercurrentai
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.