Discovers local MCP configurations (Claude Desktop, Cursor, Windsurf,
    VS Code Copilot, OpenClaw, etc.), extracts package dependencies, queries
    OSV.dev for CVEs, assesses config security (credential exposure, tool access),
    computes blast radius, and returns structured results.

    Returns:
        JSON with the complete AI-BOM report including agents, packages,
        vulnerabilities, blast radius, and remediation guidance.
    

    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. "express@4.18.2",
                 "@modelcontextprotocol/server-filesystem@2025.1.14",
                 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).
    

    Scans local MCP configurations, finds the specified CVE, and returns
    the full attack chain: which packages are affected, which MCP servers
    use those packages, which agents connect to those servers, and what
    credentials and tools are exposed.

    Args:
        cve_id: The CVE identifier (e.g. "CVE-2024-1234" or "GHSA-xxxx").

    Returns:
        JSON with blast radius details including risk_score,
        affected_servers, affected_agents, exposed_credentials, and
        exposed_tools. Returns found=false if CVE not found.
    

    This is the agent-native graph surface: Claude, Cursor, Codex,
    Windsurf, Cortex, and other MCP clients can request the same
    investigation objects used by the dashboard without scraping UI state.
    

    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.
    

    Look up risk level, known tools, credential requirements, and
    verification status for known MCP servers. The registry contains
    109+ servers with security metadata.

    Args:
        server_name: MCP server name to look up (e.g. "filesystem",
                     "@modelcontextprotocol/server-github").
        package_name: Package name to search for (e.g. "mcp-server-sqlite").
                      At least one of server_name or package_name is required.

    Returns:
        JSON with registry entry: risk_level, verified, tools,
        credential_env_vars, risk_justification. Returns found=false
        if not found.
    

    Discovers AI agents and MCP servers, extracts all package dependencies,
    and generates a standards-compliant SBOM.

    Args:
        format: SBOM format — "cyclonedx" (CycloneDX 1.6) 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.
    

    Scans local MCP configurations, maps findings to 47 security controls
    across four AI security frameworks, and returns per-control
    pass/warning/fail status with an overall compliance score.

    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 overall_score (0-100), overall_status (pass/warning/fail),
        and per-control details for OWASP LLM Top 10 (10 controls),
        OWASP MCP Top 10 (10 controls), MITRE ATLAS (13 techniques),
        and NIST AI RMF (14 subcategories).
    

    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.
    

    Checks SHA-256/SRI hashes against npm/PyPI registries and looks up
    SLSA build provenance attestations to confirm the package was built
    from its claimed source repository.

    Returns:
        JSON with integrity verification (hash match, expected vs actual)
        and provenance status (SLSA level, source repo, build trigger).
    

    Runs a new scan, then diffs it against the provided baseline (or the
    latest saved report). Shows new vulnerabilities, resolved ones, and
    changes in the package inventory.

    Returns:
        JSON with new findings, resolved findings, new/removed packages,
        and a human-readable summary.
    

    Queries the package registry (npm or PyPI) for metadata and
    cross-references against the agent-bom MCP threat intelligence registry.
    Returns trust signals including download count, CVE status, and
    registry verification.

    Args:
        package: Package name to check.
        ecosystem: 'npm' or 'pypi'. Defaults to 'npm'.

    Returns:
        JSON with name, version, ecosystem, cve_count, download_count,
        registry_verified, and trust_signals.
    

    Scans for security flaws: SQL injection, XSS, command injection,
    hardcoded credentials, insecure deserialization, path traversal, etc.
    Returns findings with CWE classifications and severity levels.

    Requires ``semgrep`` on PATH (``pip install semgrep``).
    

    Models reachability between agents, servers, credentials, tools,
    and vulnerabilities.  Answers: "If agent X is compromised, what
    else becomes reachable?"

    Returns:
        JSON with nodes, edges, lateral_paths, interaction_risks, and stats.
    

    Formats:
    - **graphml** — yEd, Gephi, NetworkX compatible with AIBOM-typed attributes
    - **cypher** — Neo4j import script with AIBOM node labels (AIAgent, MCPServer, Package, Vulnerability)
    - **dot** — Graphviz (pipe through ``dot -Tsvg``)
    - **mermaid** — embed in markdown, GitHub, Notion
    - **json** — machine-readable nodes/edges list

    Returns:
        Graph in the requested format as a string.
    

    Requires AGENT_BOM_CLICKHOUSE_URL to be set. Returns empty results if
    ClickHouse is not configured.
    

    Evaluates security posture against CIS Foundations Benchmarks:
    - AWS Foundations v3.0: 18 checks (IAM, Storage, Logging, Networking)
    - Snowflake v1.0: 12 checks (Auth, Network, Data Protection, Monitoring, Access Control)
    - Azure Security Benchmark v3.0: 10 checks (IAM, Storage, Logging, Networking, Key Vault)
    - GCP Foundation v3.0: 8 checks (IAM, Logging, Networking, Storage)

    All checks are read-only. Failed checks include MITRE ATT&CK Enterprise technique mappings.
    Requires appropriate credentials for the chosen provider.

    Returns:
        JSON with per-check pass/fail results, evidence, severity, ATT&CK techniques, and pass rate.
    

    Designed for fleet inventory data (EDR, SIEM, CSV exports) where
    you have server names but not versions. Returns per-server risk assessment
    with registry match status, risk category, tools, credentials, known CVEs,
    and a verdict (known-high-risk, known-medium, known-low, unknown-unvetted).

    Risk levels are category-derived (filesystem=high, database=medium,
    search=low), not made-up threat scores. Every field is traceable to a source.

    Returns:
        JSON with summary (total, matched, unmatched, risk breakdown)
        and per-server details.
    

    Identifies which vulnerable tools were ACTUALLY CALLED in production,
    distinguishing confirmed attack surface from theoretical risk. Produces
    risk-amplified findings: a vulnerable tool that was called 100 times is
    higher priority than one never invoked.

    Also accepts an OTel trace file (``otel_trace``) to extract ML API call
    provenance: which models were called, token usage, and deprecation advisories.

    Requires a proxy audit log (generated by running agent-bom proxy with
    the --log flag). Without an audit log, returns scan results only.

    Returns:
        JSON with correlated findings (CVE + tool call data + amplified risk),
        summary stats, uncalled vulnerable tools, and ml_api_calls provenance.