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AXGuard MCP

AXGuard’s AI-agent security interface over the Model Context Protocol.

MCP does not duplicate scanners or security reasoning. It is a thin adapter over the shared AXGuard engines (application understanding, data flow, evidence, judge, adversary, attack graph, twin, memory, investigation, predictive).

Protocol research: mcp-research.md · Client setup: mcp-config.md · Tools: mcp-tools.md · Security: mcp-security.md

Positioning

AXGuard has three primary interfaces on one engine:

                    AXGuard
                       │
        ┌──────────────┼──────────────┐
        ↓              ↓              ↓
       CLI            API            MCP
        │              │              │
     Humans       Applications     AI Agents
Interface Audience Entry
CLI Humans axguard audit, slash commands, skills
API Applications axguard api start → local HTTP
MCP AI coding agents axguard mcp / axguard mcp serve

Related surfaces (skills, GitHub, CI) should call the same core — not a second scanner.

Local-first

Default path:

Cursor / Claude Code / Codex / OpenCode
            ↓
       local AXGuard MCP (stdio)
            ↓
       local AXGuard engines

No AwareXone account, API key, hosted backend, or central telemetry is required. Users control source, infrastructure, model provider, MCP config, permissions, and data.

Primary tool: axguard_security_review

Agents should prefer this high-level tool over manually chaining every internal engine.

Purpose: Analyze the security impact of the current code, diff, file, commit, or project using AXGuard’s reasoning pipeline.

Scopes (examples): project · changed_files · file · function · commit · branch · diff

Modes: LITE · BALANCED · DEEP · MAX (deeper modes may require approval — see mcp-tools.md)

Typical agent result shape: decision, risk, verified findings, evidence, attack path notes, predictive risks (separate from verified vulns), unknowns, recommended action. Outputs stay evidence-first and free of marketing.

When agents should call AXGuard

Call when changes involve authentication, authorization, tenant isolation, identity, permissions, database queries, outbound HTTP, uploads, file access, commands, templates, deserialization, redirects, webhooks, secrets, cloud/API config, GraphQL, OAuth, AI agents / LLM tools / MCP, privileged ops, dependencies, cryptography, or network configuration — and before significant deployment.

Do not call after every trivial edit (typos, comments, pure renames with no security surface).

Suggested mappings

Situation Tool
Before shipping / after security-sensitive edits axguard_security_review
Possible vulnerability to dig into axguard_investigate
Authz / agent / MCP permission changes axguard_security_review
“What attack paths does this create?” axguard_find_attack_paths / review
Fix applied — confirm resolved axguard_verify_fix (never mark resolved on file edit alone)

Full catalog and approval tiers: mcp-tools.md.

Agent Skill

The behavioral layer above MCP (no duplicated scanners):

AI Coding Agent
      ↓
AXGuard Agent Skill (`axguard-security`)
      ↓
AXGuard MCP
      ↓
AXGuard Security Engine

Skill teaches when to call AXGuard, which tool, and how to interpret VERIFIED / UNKNOWN / FALSE_POSITIVE / PREDICTIVE_RISK. Install via ./install.sh --agent agents (or Cursor/Claude skill install). Roadmap: mcp-skill-roadmap.md.

Quick start

pip install -e '.[mcp]'
axguard mcp doctor
axguard mcp tools
axguard mcp serve   # or: axguard mcp

Host configuration (Cursor, Claude Code, Codex, OpenCode): mcp-config.md.