agent-bom

Run a full AI supply chain security scan and return an AI-BOM. Point it at a target with one of: • project_path — a local project / repo directory ("scan this repo"; must be inside home or AGENT_BOM_MCP_WORKSPACE_ROOTS) • repo_url — a public git repo URL (cloned + scanned, no checkout) • config_path — a local MCP-config directory • image — a Docker image • sbom_path — an existing CycloneDX/SPDX SBOM • package — a single package or MCP launch command (pair it with ``ecosystem`` when the spec names no launcher) With none of these, it auto-discovers local MCP clients (Claude Desktop, Cursor, Windsurf, VS Code Copilot, OpenClaw, etc.). It extracts package dependencies, looks up CVEs (online via OSV.dev and advisory sources unless offline mode is requested or configured), assesses config security (credential exposure, tool access), computes blast radius, and returns structured results. Scanning is fully static and read-only — repository and image contents are parsed, never executed. Returns: JSON. By default a bounded summary: counts (packages, agents, findings by severity/category), top findings, affected paths, and a result_id. Page any full-fidelity section with scan(result_id=..., section='findings', offset=0, limit=25). An incomplete scan (e.g. vulnerability source unavailable) is returned as an error result whose body is still JSON.

Check a specific package for known CVEs before installing. Queries OSV.dev for vulnerabilities in the given package. Use this before installing an MCP server or dependency to verify it is safe. Args: package: Package name with optional version, e.g. "[email protected]", "@modelcontextprotocol/[email protected]", or just "requests" (resolves @latest). ecosystem: Package ecosystem — "npm", "pypi", "go", "cargo", "maven", "nuget", "rubygems", "composer", "swift", "pub", "hex", "conda", "deb", "apk", or "rpm". Defaults to "npm". Returns: JSON with package, version, ecosystem, vulnerability count, and vulnerability details (id, severity, cvss, fix version, summary).

Look up one advisory from the local threat-intel database.

Evaluate a security policy against current scan results. Runs a scan, then evaluates the provided policy rules against the findings. Policies can gate on severity thresholds, CISA KEV status, AI risk flags, credential exposure, and denied packages. Args: policy_json: JSON string containing policy rules. Example: {"rules": [{"id": "no-critical", "severity_gte": "critical", "action": "fail"}, {"id": "no-kev", "kev": true, "action": "fail"}]} Returns: JSON with passed (bool), violations list, failure_count, and warning_count.

Generate a Software Bill of Materials (SBOM) for your AI agent setup. Discovers AI agents and MCP servers, extracts all package dependencies, and generates a standards-compliant SBOM. Args: format: SBOM format — "cyclonedx" (CycloneDX 1.7) or "spdx" (SPDX 3.0). config_path: Path to a specific MCP config directory. If not provided, auto-discovers all local agent configs. Returns: JSON string containing the SBOM in the requested format.

Return compliance posture with its source: the tenant's persisted evidence, or a labelled local scan. With completed scans in the control plane, ``summary`` is exactly ``/v1/compliance/summary``; ``framework`` adds ``/v1/compliance/{framework}`` and ``control`` pages the driving finding ids from ``/v1/findings?framework=&control=``. Otherwise (or with source='local_scan') it scans local MCP configurations and labels that scope.

Generate a remediation plan for vulnerabilities in your AI agent setup. Scans for vulnerabilities, then generates actionable fix commands for each affected package (npm install, pip install), credential scope reduction guidance, and reports on unfixable vulnerabilities. Args: config_path: Path to a specific MCP config directory. If not provided, auto-discovers all local agent configs. image: Docker image reference to scan (e.g. "nginx:1.25"). Returns: JSON with package_fixes (upgrade commands by ecosystem), credential_fixes (scope reduction steps), and unfixable items.

Return the attack-path queue in the dashboard's order, with why each path ranks where it does. Ranking is ``ranking.order`` (evidence tier, KEV, severity, evidence confidence, composite risk, credential count); every path carries ``rank``, ``evidenceTier``, ``isKev``, ``severity``, ``evidenceConfidence`` and ``rankingFactors``. Totals, filters and cursor pages match ``/v1/graph/attack-paths``. With ``result_id`` the paths are that session scan result's, unfiltered and labelled ``session_scan_result``.