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Glama
koda-ai-studio

agent-bom

by koda-ai-studio
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
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prompts
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resources
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Tools

Functions exposed to the LLM to take actions

NameDescription
scanA

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.

checkA

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. "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).
intel_lookupA

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

policy_checkA

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_sbomA

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.
complianceA

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.
remediateA

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.
exposure_pathsA

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``.

Prompts

Interactive templates invoked by user choice

NameDescription
quick-auditRun a complete security audit of your AI agent setup
pre-install-checkCheck an MCP server package for vulnerabilities before installing
remediation-planDraft a human-reviewed remediation plan without modifying files

Resources

Contextual data attached and managed by the client

NameDescription
profiles_resourceTask profiles and startup commands; no tool schemas or credentials
policy_template_resourceGet a default security policy template for agent-bom. Returns a ready-to-use policy with common rules: block critical CVEs, flag CISA KEV entries, warn on unverified servers, and limit credential exposure.
tool_metrics_resourceReturn bounded MCP tool execution metrics for observability.
framework_controls_resourceSummarize framework coverage and the evidence surfaces behind each claim.
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4/5.0

Scored across 8 tools

Disambiguation4/5

Most tools target distinct operations: scan (full audit), check (single package CVE), generate_sbom (SBOM export), policy_check, remediate, compliance, intel_lookup, exposure_paths. However, scan and generate_sbom both 'discover agents and extract dependencies', and policy_check/remediate internally run scans, creating mild overlap in scope. Descriptions do help distinguish them, keeping ambiguity low.

Naming Consistency3/5

Mix of verb_noun (generate_sbom, intel_lookup, policy_check, exposure_paths) and bare nouns/verbs (scan, check, compliance, remediate). Consistent snake_case style throughout keeps it readable, but the verb-vs-noun convention is not uniform.

Tool Count5/5

Eight tools is well within the ideal 3-15 range for a security scanning server. Each tool covers a distinct stage of the security workflow (scan, check, SBOM, policy, remediation, compliance, intel, attack paths), so each earns its place.

Completeness4/5

The surface covers the full security lifecycle: scanning, SBOM generation, CVE lookup, policy gating, remediation, compliance, and attack-path analysis. Minor gaps exist—no tools for managing/saving policies, comparing baseline scans, or suppressing/waiving findings—but core workflows are covered.

Maintenance

ActivityActive
ResponsivenessResponsive

Resources