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  1. Home
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  3. Aegis
Aegis logo
Health: ActiveRecent health check succeeded.Last checked 9/9/2026, 11:31:54 AM

Aegis

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View Repository15 GitHub StarsTotal stargazers on GitHub for the source repository (15 stars).Visit Website
securitymcpgovernanceauditagent-tools

Governs AI agent and MCP tool calls with YAML policies, approvals, risk checks, guardrails, and tamper-evident audit logs.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent โ€” or use 1-click editor setup below.

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We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag โ€” we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON โ–พ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "acacian-aegis": {
      "command": "uvx",
      "args": [
        "--from"
      ]
    }
  }
}

๐Ÿ’ก Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing Alternatives๐Ÿ”’ More in Security

Overview

Acacian/aegis MCP server applies policy-based governance to MCP and other agent tool calls through a transport proxy or framework adapters. It supports YAML rules, approval decisions, deterministic guardrails, delegation tracking, risk assessment, and tamper-evident auditing. Reach for it when agents need consistent controls across MCP servers or multiple supported agent frameworks without changing each agent's application code.

Use cases

โ€ขEnforce approval or blocking policies for MCP tool calls
โ€ขAudit delegated actions across multi-agent workflows
โ€ขMask or detect sensitive information in agent activity
โ€ขApply shared governance across supported agent frameworks

Key features

โ€ขYAML policy rules with approval gates
โ€ขMCP stdio and HTTP transport proxy
โ€ขPrompt-injection, PII, leak, and toxicity checks
โ€ขTrust delegation and risk assessment
โ€ขMerkle-chained audit logging
โ€ขMulti-framework auto-instrumentation

Capabilities & Tool Schemas

Inspect callable tools, capabilities, and parameters exposed to AI agents by Aegis.

Extracted Tool Capabilities
YAML policy rules with approval gates
MCP stdio and HTTP transport proxy
Prompt-injection, PII, leak, and toxicity checks
Trust delegation and risk assessment
Merkle-chained audit logging
Multi-framework auto-instrumentation

How Aegis works

What Acacian/aegis MCP server does

Acacian/aegis MCP server provides a governance layer for agent actions, including MCP calls, tool invocations, model requests, and HTTP activity. Its policy model represents each operation as an Action and can evaluate declarative rules that match an action, assess risk, and choose an auto, approve, or block outcome.

The project combines several governance primitives rather than limiting checks to generated text. These include prompt-injection, PII, prompt-leak, and toxicity checks; role-based access control; rate limits; cost budgets; anomaly and drift detection; delegation-chain validation; selection auditing; and justification-gap scoring. The README describes these as deterministic controls, with optional semantic or model-based detectors able to be added to the guardrail engine.

How it works

For MCP, Acacian/aegis MCP server operates as a transport-layer proxy for servers using stdio or HTTP. This allows governance to sit between the agent and the MCP server without patching the MCP server itself. For supported agent frameworks, the runtime detects installed integrations and instruments only those that are present.

The common contract includes declared, assessed, and delegation-chain claims. A policy can compare an agent's stated impact with Aegis's independent assessment, while delegation rules enforce a non-increasing trust constraint across hand-offs. Audit events are appended to a Merkle-chained log, with SQLite, JSONL, webhook, and Ed25519-signed audit options described in the project materials.

Supported adapters cover Google ADK, CrewAI, Pydantic AI, OpenAI Agents SDK, LangChain, OpenAI API, Anthropic API, LiteLLM, Google GenAI, LlamaIndex, Instructor, and DSPy. Additional wrappers cover raw HTTP through httpx and browser activity through Playwright. Custom integrations can use the BaseAdapter interface.

Setup and configuration

Install the Python package with pip install agent-aegis, then initialize instrumentation with aegis.auto_instrument(). The automatic setup looks for installed frameworks and applies the relevant adapters; the README states that these integrations do not require hard dependencies for every framework.

For an MCP deployment, use the project as a proxy for an MCP server over stdio or HTTP. The supplied material identifies the proxy mode but does not provide a complete proxy command, configuration file schema, or MCP client-specific setup example. YAML policies are the main configuration mechanism, defining action matching, risk handling, and approval behavior.

Tools and capabilities

Acacian/aegis MCP server supports:

  • YAML policy evaluation with automatic approval, human approval, or blocking outcomes.
  • Deterministic checks for prompt injection, PII, prompt leakage, and toxicity.
  • Trust-aware delegation chains for multi-agent workflows.
  • Risk scoring based on six-dimensional impact claims and assessments.
  • Selection audits that examine excluded options as well as selected tools.
  • Rate limits, cost budgets, anomaly scoring, drift detection, cascade guards, and kill switches through composed primitives.
  • Append-only, tamper-evident audit records with Merkle chaining.
  • SQLite, JSONL, webhook, and cryptographically signed audit outputs.

Limitations and notes

The README lists broad framework coverage, but adapter behavior differs: some integrations use native hooks, some patch framework methods, and MCP uses a proxy rather than patching. The provided material does not specify a standalone MCP executable, client configuration examples, environment variables, or hosted endpoint. It also does not identify Claude Desktop, Cursor, Windsurf, or Cline as compatible clients.

Read the full README โ†’View source on GitHub โ†’

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks โ€” not a rating.

GitHub stars
15
Stargazers on the source repository.
Last commit
14d ago
Most recent push to the default branch.
Directory activity
4 views
Config copies, upvotes, and views on AllMCPs.

Reviews

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Frequently Asked Questions about Aegis

The README gives `pip install agent-aegis` for installing the Python package and `aegis.auto_instrument()` for enabling instrumentation. It does not provide a standalone MCP server command.

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Technical Specs & Signals

Category๐Ÿ”’Security
PricingFree
More technical detailsExpand โ–พ
TransportSTDIO
RuntimePython
AuthNo auth required
LicenseMIT
ClientsWindsurf, Cline / VS Code
Last updatedSep 4, 2026
9/9 checks healthy over the last 33d
Views4
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars15
GitHub Star CountTotal stargazers on GitHub representing community popularity (15 stars).
Last commit14d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Aug 29, 2026
53Quality signal: Good ยท 53/100How this signal is calculated โ–พ
Server availabilityNot measured

Not scored for repo-hosted servers โ€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools23/30
Adoption & activity6/15
Community engagement1/10

A guidance signal from public completeness & health data โ€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 18d ago via OSV.dev ยท --from (PyPI)

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