Skip to main content
Glama

Security Scan

scan
Read-onlyIdempotent

Run an AI supply chain security scan that discovers local MCP configurations, extracts dependencies, checks for CVEs and credential exposure, computes blast radius, and returns a structured AI-BOM report.

Instructions

Run a full AI supply chain security scan.

    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.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
config_pathNoPath to MCP client config directory. Auto-discovers all if omitted.
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.
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
Behavior3/5

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

Annotations already indicate read-only, destructive=false, idempotent. The description adds behavioral details like discovering local MCP configs and querying OSV.dev, but does not discuss network usage, disk impact, or other side effects. Adequate but not extensive.

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 a concise 4-sentence paragraph with a clear return statement. It is front-loaded with the main purpose. Slightly verbose but well-structured for readability.

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 14 parameters, full schema coverage, and an output schema, the description provides a solid overview of the tool's capabilities. It covers key aspects like local discovery, CVE querying, and report structure. Minor gap: does not explain fail_severity/warn_severity relationship explicitly, but schema handles that.

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% with descriptions for all 14 parameters. The tool description repeats the overall scanning process but does not add new meaning beyond schema. Baseline 3 is appropriate.

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 runs a full AI supply chain security scan, listing specific actions (discovers MCP configs, extracts dependencies, queries CVEs, assesses config security, computes blast radius) and outputs a structured AI-BOM report. It is distinct from sibling tools like code_scan or model_file_scan.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives. It does not specify prerequisites, excluded scenarios, or when not to use it. The description lacks context about appropriate use cases.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/msaad00/agent-bom'

If you have feedback or need assistance with the MCP directory API, please join our Discord server