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Security Scan

scan
Read-onlyIdempotent

Scan an AI supply chain to generate an AI-BOM with dependency vulnerabilities, configuration risks, and blast radius analysis from repos, configs, images, or SBOMs.

Instructions

Run a full AI supply chain security scan and return an AI-BOM.

    Point it at a target with one of:
      • repo_url     — a public git repo URL (cloned + scanned, no checkout)
      • config_path  — a local project / MCP-config directory
      • image        — a Docker image
      • sbom_path    — an existing CycloneDX/SPDX SBOM
      • package      — a single package or MCP launch command
    With none of these, it auto-discovers local MCP clients (Claude Desktop,
    Cursor, Windsurf, VS Code Copilot, OpenClaw, etc.).

    It extracts package dependencies, queries OSV.dev for CVEs, 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 with the complete AI-BOM report including agents, packages,
        vulnerabilities, blast radius, and remediation guidance.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repo_urlNoPublic git repository URL to clone and scan, e.g. 'https://github.com/org/repo'. Maps the repo's dependencies, project structure, secrets, IaC, and AI/MCP usage into an AI-BOM. Static and read-only: the repository is shallow-cloned into a temporary directory, scanned without ever executing its code, then deleted. The fastest way to point this tool at a target — no local checkout required.
config_pathNoLocal directory to scan — a project root or an MCP client config directory. Auto-discovers all installed MCP clients if omitted. Mutually exclusive with repo_url.
imageNoDocker image to scan (e.g. 'nginx:1.25', 'ghcr.io/org/app:v1').
sbom_pathNoPath to existing CycloneDX or SPDX JSON SBOM file to ingest.
packageNoDirect package or MCP launch command to scan, e.g. 'npx @modelcontextprotocol/server-filesystem@2025.1.14' or '@modelcontextprotocol/server-filesystem'.
enrichNoEnable NVD CVSS, EPSS probability, and CISA KEV enrichment.
offlineNoUse the local vulnerability DB only and skip registry, OSV, GHSA, and NVIDIA network lookups.
scorecardNoEnrich packages with OpenSSF Scorecard scores (requires resolvable GitHub repos).
transitiveNoResolve transitive dependencies for npx/uvx packages.
verify_integrityNoVerify package SHA-256/SRI hashes and SLSA provenance against registries.
fail_severityNoReturn failure status if vulns at this severity or higher: critical, high, medium, low.
warn_severityNoReturn warning status (gate_status=warn, exit 0) when vulns at this severity or higher exist. Use with fail_severity for two-tier CI gates, e.g. warn_severity='medium', fail_severity='critical'.
auto_update_dbNoExplicitly refresh the local vuln DB when older than the daily freshness target before scanning.
db_sourcesNoComma-separated DB sources to sync before scanning (e.g. 'nvd,ghsa,osv,epss,kev').
output_formatNoOutput format: 'json' (default), 'sarif', 'cyclonedx', 'spdx', 'junit', 'csv', or 'markdown'.json
policyNoPolicy object to evaluate alongside scan results, e.g. {"rules": [{"id": "no-critical", "severity_gte": "critical", "action": "fail"}]}.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description reinforces this by stating 'Scanning is fully static and read-only — repository and image contents are parsed, never executed.' It adds detailed behavior: shallow-clone, scan, delete. No contradictions, and the description adds extra context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear opening sentence, a bullet list for target options, and a concise explanation of scanning methodology. Each sentence earns its place, though it could be slightly shorter. The front-loading of the main action and target options is effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (16 parameters, many sibling tools), the description provides a good overview: what the scan does, how to point it, and what it returns. It explains auto-discovery and static nature. The output schema exists, so return values are covered. However, it doesn't detail the enrichment or policy parameters, but schema covers them. Overall, context is adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description lists the target parameters in a bullet list but doesn't add significant meaning beyond the schema descriptions. It mentions 'Mutually exclusive with repo_url' for config_path, but that is already in the schema. No new semantic insight for other parameters like enrich, offline, etc. Thus, description provides marginal added value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Run a full AI supply chain security scan and return an AI-BOM.' It specifies the verb (run/scan), resource (AI supply chain), and output (AI-BOM). It provides enough detail to distinguish it from sibling tools like code_scan or fleet_scan by emphasizing the supply chain scope and specific scanning actions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly lists five ways to target a scan (repo_url, config_path, image, sbom_path, package) and states that with none provided it auto-discovers local MCP clients. It also mentions that the scan is static and read-only. While it doesn't explicitly say when not to use it, the alternatives are clear. Lacks explicit comparisons to siblings, but the context is sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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