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msaad00

agent-bom

by msaad00
OverviewSchemaRelated ServersScoreDiscussions
Python

Generate SBOM

generate_sbom
Read-onlyIdempotent

Generate standards-compliant SBOMs for AI agent setups: auto-discover MCP servers, extract dependencies, and produce CycloneDX or SPDX files.

Instructions

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNoSBOM format: 'cyclonedx' (CycloneDX 1.7) or 'spdx' (SPDX 3.0).cyclonedx
config_pathNoPath to MCP client config directory. Auto-discovers all if omitted.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint, idempotentHint, and destructiveHint=false. The description adds that the tool discovers agents and servers, extracts dependencies, and generates a standards-compliant SBOM, which is consistent with read-only behavior.

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

Conciseness5/5

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

The description is concise, front-loaded with the purpose, and uses a clear structure with bullet points for arguments and return. Every sentence contributes meaning.

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

Completeness5/5

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

Given the presence of an output schema (mentioned in signals) and two optional parameters, the description provides complete context: what the tool does, how parameters work, and the return format. No gaps.

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

Parameters4/5

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

Schema description coverage is 100% for both parameters. The description adds value by specifying the exact format values ('cyclonedx' and 'spdx') and clarifying config_path auto-discovery behavior, which goes beyond the schema.

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 generates a Software Bill of Materials (SBOM) for AI agent setups, with specific details on discovering agents, MCP servers, and extracting dependencies. It distinguishes itself from sibling tools like 'scan' or 'inventory' by focusing on SBOM generation.

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 states when to use the tool (to generate an SBOM) and explains config_path auto-discovery. However, it does not mention when not to use it or compare to alternatives among siblings.

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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